Feedback method for interference alignment in wireless network

ABSTRACT

A feedback method for interference alignment in wireless network having K transmitters and K receivers is provides. The method comprising: transmitting, performed by transmitter n−1, a precoding vector of transmitter n−1 (n is a integer between 2 and K−1) to receiver n+1; calculating, performed by the receiver n+1, a precoding vector of transmitter n using the precoding vector of transmitter n−1; and transmitting, performed by the receiver n+1, the precoding vector of transmitter n to transmitter n.

TECHNICAL FIELD

The present invention relates to wireless communication, and more particularly, to a feedback method for interference alignment in wireless network.

BACKGROUND ART

In a wireless network with multiple interfering links, interference alignment (IA) is used. IA is a transmission scheme achieving linear sum capacity scaling with the number of data links, at high SNR. With IA, each transmitter designs the precoder to align the interference on the subspace of allowable interference dimension over the time, frequency or space dimension, where the dimension of interference at each receiver is smaller than the total number of interferers. Therefore, each receiver simply cancels interferers and acquires interference-free desired signal space using zero-forcing (ZF) receive filter.

Most of conventional research on IA is considered to achieve the maximum gain of IA using the infinite selectivity over symbol extensions, which is unrealistic in practical wireless networks. Therefore, the recent studies on MIMO IA focused on the design of IA precoder using a finite space dimension over one transmission slot, which is called MIMO constant channel.

The difficulties of IA in MIMO constant channel is to derive the closed form solution of the IA precoder. Even though the closed form solution in 3 user and some special cases are proposed, it is still unsolved in general case of the number of antennas, user and desired DoF (degree of freedom).

Despite fruitful research and discussion on IA with various aspects, IA is far from being practical. A key challenge for implementing IA techniques is their requirement of perfect and global CSI. This requirement potentially will lead to unacceptable network overhead given the existence of many feedback links and the high-fidelity CSI transmitted over each link.

SUMMARY OF INVENTION Technical Problem

It is an object of the present invention to provide a feedback method of interference alignment in wireless network for reducing network CSI overhead.

Solution to Problem

asdsd

Advantageous Effects of Invention

In accordance with the present invention, a significant reduction of network overhead compared with conventional feedback framework for IA is provided.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a framework of the present invention.

FIG. 2 shows a CSI exchange method(method 1).

FIG. 3 illustrates a conventional full-feedback method.

FIG. 4 shows an example of star feedback method method(method 3).

FIG. 5 shows the procedure of CSI exchange method in case that K is 4.

FIG. 6 shows the procedure of CSI exchange method based on precoded RS in case that K is 4.

FIG. 7 illustrates feedback information at each receiver for efficient interference alignment method for 3 cell MIMO interference channel with limited feedback.

FIG. 8 shows sum rate performance, total number of stream of other method.

FIG. 9 shows probability of turning off for each transmitter.

FIG. 10 shows a binary power control method.

FIG. 11 shows the result of the binary power control method.

FIG. 12 shows effect of interference misalignment due to quantization of feedback CSI.

MODE FOR THE INVENTION

In a wireless network with multiple interfering links, interference alignment (IA) uses precoding to align at each receiver the interference components from different sources. As a result, in the high SNR regime, the network capacity scales logarithmically with the signal-to-noise-ratio (SNR) and linearly with half of the number of parallel subchannels, called degrees of freedom (DOF). In addition, in the presence of multi-antennas, the capacity also scales linearly with the spatial DOF per link. IA has been proved to be optimal in terms of DOF.

This motivates extensive research on IA methods for various types of channels and settings, including MIMO channel, cellular networks, distributed IA, IA in the signal space and limited feedback. However, most existing works on IA rely on the impractical assumption that each transmitter in an IA network requires perfect CSI of all interference channels. Some preliminary results have been obtained on the scaling laws of numbers of CSI feedback bits with respect to the SNR under the IA constraint. However, there exist no designs of practical CSI feedback algorithms for IA networks.

To facilitate the description of present invention, the framework of the present invention will be explained firstly.

FIG. 1 shows a framework of the present invention.

Referring FIG. 1, the framework of the present invention comprises a cooperative CSI exchange, a transmission power control and a interference misalignment control.

1. Cooperative CSI exchange: Transmit beamformers will be aligned progressively by iterative cooperative CSI exchange between interferers and their interfered receivers. In each round of exchange, a subset of transmitters receive CSI from a subset of receivers and relay the CSI to a different subset of receivers. Based on this approach, CSI exchange methods are optimized for minimizing the number of CSI transmission links and the dimensionality of exchanged CSI, resulting in small network overhead.

2. Transmission power control: Given finite-rate CSI exchange, quantization errors in CSI cause interference misalignment. To control the resultant residual interference, methods are invented to support exchange of transmission power control (TPC) signals between interferers and receivers to regulate the transmission power of interferers under different users' quality-of-service (QoS) requirements. Since IA is decentralized and links are coupled, multiple rounds of TPC exchange may be required.

3. Interference misalignment control: Besides interferers' transmission power, another factor that influences interference power is the degrees of interference misalignment (IM) of transmit beamformers, which increase with CSI quantization errors and vice versa. The IM degrees are regulated by adapting the resolutions of feedback CSI to satisfy users' QoS requirements. This requires the employment of hierarchical codebooks at receivers that support variable quantization resolutions. Designing IM control policies is formulated as an optimization problem of minimizing network feedback overhead under the link QoS constraints. Furthermore, the policies are optimized using stochastic optimization to compress feedback in time.

In addition, it will be described a star feedback method, a efficient interference alignment for 3-cell MIMO interference channel with limited feedback and binary power control for IA with limited feedback.

4. Star Feedback Method: For achieving IA, each precoder is aligned to other precoders and thus its computation can be centralized at central unit, which is called CSI base station (CSI-BS). In star feedback method, CSI-BS gathers CSI of interference from all receivers and computes the precoders. Then, each receiver only feeds CSI back to CSI-BS, not to all transmitters. For this reason, star feedback method can effectively scale down CSI overhead compared with conventional feedback method.

5. Efficient interference alignment for 3 cell MIMO interference channel with limited feedback: Conventional IA solution for achieving the optimal DOF and a special case IA solution requires product of all cross link channel information since all precoders are coupled. When limited feedback channel is assumed, channel matrix product arises a critical problem that there is a K-fold increase in error of initial precoder due to multiplied and summed channel quantization errors. IA solution for achieving the optimal DOF may not be optimal in practical number of feedback bits.

To avoid the product of channel matrices and get better performance, a DOF following method is proposed. If one node does not use a DOF, IA solution is separated to several independent equation, resulting in avoiding the product of all cross link channel matrices and reducing feedback overhead.

6. Binary Power Control for Interference Alignment with Limited Feedback: In practical interference alignment (IA) systems, beamformers(i.e. precoding vector or matrix) are computed based on limited feedback of channel state information (CSI). Suppressing residual interference caused by CSI inaccuracy can lead to overwhelming network feedback overhead. An alternative solution for coping with residual interference is to control the transmission power of all transmitters such that the network throughput is maximized. Finding the optimal power control policy for multi-antenna IA system requires solving a non-convex optimization problem and has no known closed-form solution. To overcome this difficulty, it is proposed the simple binary power control (BPC) method, which turns each the transmission either off or on with the maximum power. Furthermore, we design a distributive algorithm for realizing BPC. Simulation shows that BPC achieves sum throughput closed to the centralize optimal solution based on an exhaustive search.

Firstly, a system model is described.

I. System Model.

we introduce the system model of K user MIMO interference channel and define the metric of network overhead that is required for implementing IA precoder.

Consider K user interference channel, where K transmitter-receiver links exist on the same spectrum and each transmitter send an independent data stream to its corresponding receiver while it interferers with other receivers. Assuming every transmitter-receiver node is equipped with M antennas, the channel between transmitter and receiver is modeled as M×M independent MIMO block channel consisting of path-loss and small-scale fading components. Specifically, it is denoted the channel from the j-th transmitter and the k-th receiver as d_(kj) ^(−α/2) H^([kj]), where a is the path-loss exponent, d_(kj) is the distance between transmitter and receiver and H^([kj]) is M×M matrix of independently and identically distributed circularly symmetric complex Gaussian random variables with zero mean and unit variance, denoted as CN(0,1).

Let denote v^([k]) and r^([k]) as a (M×1) beamforming vector and receive filter at the k-th transceiver, where ∥v^([k])∥²=∥r^([k])∥²=1. A beamforming vector can be called other terminologies such as beamformer or a precoding vector. Then, the received signal at the k-th receiver can be expressed as below equation.

$\begin{matrix} {y^{\lbrack k\rbrack} = {{H^{\lbrack{kk}\rbrack}v^{\lbrack k\rbrack}s_{k}} + {\sum\limits_{j \neq k}{H^{{\lbrack{kj}\rbrack}\;}v^{\lbrack j\rbrack}s_{j}}} + n_{k}}} & \left\lbrack {{equation}\mspace{14mu} 1} \right\rbrack \end{matrix}$

and the sum rate is calculated as below equation.

$\begin{matrix} {R_{sum} = {\sum\limits_{k = 1}^{K}{\log_{2}\left( {1 + \frac{\frac{P}{d_{kk}^{\alpha}}{{r^{{\lbrack k\rbrack}^{\dagger}}H^{\lbrack{kk}\rbrack}v^{\lbrack k\rbrack}}}^{2}}{{\sum\limits_{k \neq j}{\frac{P}{d_{kj}^{\alpha}}{{r^{{\lbrack k\rbrack}^{\dagger}}H^{\lbrack{kj}\rbrack}v^{\lbrack j\rbrack}}}^{2}}} + \sigma^{2}}} \right)}}} & \left\lbrack {{equation}\mspace{14mu} 2} \right\rbrack \end{matrix}$

where s_(k) denotes a data symbol sent by the k-th transmitter with E[|s_(k)|²]=P and n_(k) is additive white Gaussian noise (AWGN) vector with covariance matrix σ²I_(M).

Under the assumption of perfect and global CSI, IA aims to align interference on the lower dimensional subspace of the received signal space so that each receiver simply cancels interferers and acquires K interference-free signal space using zero-forcing (ZF) receive filter satisfying following equation.

r^([k]†)H^([kj])v^([j])=0, ∀k≠j   [equation 3]

Therefore, the achievable sum rate of IA is computed by below equation.

$\begin{matrix} {R_{sum} = {\sum\limits_{k = 1}^{K}{\log_{2}\left( {1 + {\frac{P}{\sigma^{2}}d_{kk}^{- \alpha}{{r^{{\lbrack k\rbrack}^{\dagger}}H^{\lbrack{kk}\rbrack}v^{\lbrack k\rbrack}}}^{2}}} \right)}}} & \left\lbrack {{equation}\mspace{14mu} 4} \right\rbrack \end{matrix}$

It is assumed that each receiver, say the m-th receiver, perfectly estimates all interference channels, namely the set of matrices {H^([mk])}_(k=1) ^(K). Also, it is considered the case where CSI can be sent in both directions between a transmitter and a receiver.

II. CSI Feedback Methods.

In MIMO constant channel, the closed form solution of IA in K=3 user is presented in prior arts. And the solution of K=M+1 with a single data stream at transmitter-receiver pairs is proposed in prior arts. However, such closed form solutions in general K user interference channel are still open problem. Here, three CSI feedback methods, namely the 1. CSI exchange method(method 1), 2. modified CSI exchange method(method 2) and 3. star feedback method(method 3) are described for achieving IA under the constraints K=M+1. Also, we compare the efficiency of each feedback method with sum overhead defined as the total number of complex CSI coefficients transmitted in the network for a given channel realization expressed in equation 5.

$\begin{matrix} {N - {\sum\limits_{m,{k \in {\{{1,2,\ldots,K}\}}}}\; \left( {N_{TR}^{\lbrack{mk}\rbrack} + N_{RT}^{\lbrack{mk}\rbrack}} \right)}} & \left\lbrack {{equation}\mspace{14mu} 5} \right\rbrack \end{matrix}$

In equation 5, N_(TR) ^([mk]) denotes the integer equal to the number of complex CSI coefficients sent from the k-th receiver to the m-th transmitter, and N_(RT) ^([mk]) from the k-th transmitter to the m-th receiver.

FIG. 2 shows a CSI exchange method in the case of K=4(method 1).

Assuming each pair of transmitter-receiver gets one multiplexing gain under the constraint K=M+1, the set of interference {H^([mk])v^([k])}_(k=1;k≠m) ^(K) at each receiver should be aligned on the subspace of dimension at most M−1 to satisfy IA condition which described in equation 3. That is to say, at least two of all interferers are designed on the same subspace at the receiver as below equation.

$\begin{matrix} \begin{matrix} {{{span}\mspace{14mu} \left( {H^{\lbrack{{1\; K} - 1}\rbrack}v^{\lbrack{K - 1}\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack{1\; K}\rbrack}v^{\lbrack K\rbrack}} \right)}} & {{at}\mspace{14mu} {receiver}\; 1} \\ {{{span}\mspace{14mu} \left( {H^{\lbrack{2\; K}\rbrack}v^{\lbrack K\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack 21\rbrack}v^{\lbrack 1\rbrack}} \right)}} & {{at}\mspace{14mu} {receiver}\; 2} \\ {{{span}\mspace{14mu} \left( {H^{\lbrack 31\rbrack}v^{\lbrack 1\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack 32\rbrack}v^{\lbrack 2\rbrack}} \right)}} & {{at}\mspace{14mu} {receiver}\; 3} \\ { \vdots} & \; \\ {{{span}\mspace{14mu} \left( {H^{\lbrack{{KK} - 2}\rbrack}v^{\lbrack{K - 2}\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack{{KK} - 1}\rbrack}v^{\lbrack{K - 1}\rbrack}} \right)}} & {{at}\mspace{14mu} {receiver}\; K} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 6} \right\rbrack \end{matrix}$

In equation 6, span(A) denotes the vector space that spanned by the columns of A and each equation satisfies that span (H^(|k+2k|)v^(|k|))=span (H^(|k+2k+1|)v^(|k+1|)) (i.e. span(v^(|k|))=span (H^([k+2k]))⁻¹H^([k+2k+1])v^([k+1]))). As concatenating {span (v^([k]))}_(k=1) ^(K), IA beamformers are computed by below equation and then normalized to have unit norm.

$\begin{matrix} {{{v^{\lbrack 1\rbrack} = {{any}\mspace{14mu} {eigenvector}\mspace{14mu} {of}\mspace{14mu} \left( H^{\lbrack 21\rbrack} \right)^{- 1}{H^{\lbrack{2\; K}\rbrack}\left( H^{\lbrack{1\; K}\rbrack} \right)}^{- 1}H^{\lbrack{{1\; K} - 1}\rbrack}{\ldots \left( H^{\lbrack 32\rbrack} \right)}^{- 1}H^{\lbrack 31\rbrack}}}v^{\lbrack 2\rbrack} = {\left( H^{\lbrack 32\rbrack} \right)^{- 1}H^{\lbrack 31\rbrack}v^{\lbrack 1\rbrack}}}\mspace{50mu} \vdots {v^{\lbrack{K - 1}\rbrack} = {\left( H^{\lbrack{{K\mspace{11mu} K} - 1}\rbrack} \right)^{- 1}H^{\lbrack{{K\mspace{11mu} K} - 2}\rbrack}v^{\lbrack{K - 2}\rbrack}}}{v^{\lbrack K\rbrack} = {\left( H^{\lbrack{1\; K}\rbrack} \right)^{- 1}H^{\lbrack{{1\; K} - 1}\rbrack}v^{\lbrack{K - 1}\rbrack}}}} & \left\lbrack {{equation}\mspace{14mu} 7} \right\rbrack \end{matrix}$

The solution of k-th beamformer v^([k]) in equation 7 is serially determined by the product of pre-determined v^([k−1]) and channel matrix (H^([k+1k−1]))⁻¹H^([k+1k−1]).

Hereinafter, using the property that v^([k]) is serially determined, CSI exchange method(method 1) will be described. Hereinafter, it is assumed that K=4, M=3.

1. First, transmitters determine which interferers are aligned on the same subspace at each receiver. Then, each transmitter informs its corresponding receiver of the indices of two interferers to be aligned in the same direction. This process is called set-up.

2. Receiver k(∀k≠3) feeds back the product matrix (H^([21]−1)H^([2K]), . . . , (H^([32]))⁻¹H^([31]) to receiver 3 which determines v^([1]) as any eigenvector of (H^([21]))⁻¹H^([2K])(H^([1K]))⁻¹H^([1K−1]) . . . (H^([32]))⁻¹H^([) ³¹]. Then, v^([1]) is fed back to transmitter 1.

3. Computation of v^([2]), . . . , v^([K−1]): Transmitter k−1 feeds forward v^([k) ^(−1]) to receiver k+1. Then, receiver k+1 calculates v^([k]) using equation 7 and feeds back v^([k]) to transmitter k. This process is performed for k=2 to K−1.

4. computation of v^([K]).

Transmitter K−1 feeds forward v^([K−1]) to receiver 1. Then, receiver 1 calculates v^([K]) using equation 7 and feeds back v^([K]) to transmitter K.

In CSI exchange method, two interferers are aligned at each receiver among K−1 interferers. Therefore, the receiver requires the indices of two interferers to be aligned before starting the computation and exchange procedure of {v^([1]), . . . , v^([K])}. The signals for a set-up are forwarded through a control channel from transmitter to corresponding receiver. These set-up signals are forwarded through B_(setup) bits control channel from transmitter to corresponding receiver and its overhead is computed in following lemma.

Lemma 1. The set-up overhead for the CSI exchange method is given as below equation.

$\begin{matrix} {B_{setup} = \left\lceil {K \cdot {\log_{2}\left( \frac{\left( {K - 1} \right)\left( {K - 2} \right)}{2} \right)}} \right\rceil} & \left\lbrack {{equation}\mspace{14mu} 8} \right\rbrack \end{matrix}$

Each receiver has K−1 interferers from other transmitters. Therefore, _(K−1)C₂ groups of aligned interferers exist at each receiver, where _(K−1)C₂=(K−1)(K−2)/2. To inform each of K receivers about the group of aligned interferers, total

$\left\lceil {K \cdot {\log_{2}\left( \frac{\left( {K - 1} \right)\left( {K - 2} \right)}{2} \right)}} \right\rceil$

bits are required for the set-up signaling.

Once the aligned interferers are indicated at the receiver, each receiver feeds back CSI of the selected interferers in given channel realization and the beamformers v^([1]), v^([2]), . . . , v^([K]) are sequentially determined by method 1. The overhead for the CSI exchange method can be measured in terms of the number of exchanged complex channel coefficients. Such overhead is specified in the following proposition.

Proposition 1.

For the CSI exchange method in MIMO channel, the network overhead is given as below equation.

N _(EX)=(K−1)M ²+(2K−1)M   [equation 9]

All receivers inform CSI of the product channel matrices to receiver 3, which comprises (K−1)M² nonzero coefficients. After computing the beamformer v^([1]), M nonzero coefficients are required to feed it back to transmitter 1. Each beamformer is determined by iterative precoder exchange between transmitters and interfered receivers. In each round of exchange, the number of nonzero coefficients of feedforward and feedback becomes 2M. Thus, total network overhead comprises (K−1)M²+(2K−1)M.

FIG. 3 illustrates a conventional full-feedback method.

For comparison, the sum overhead for the convention CSI feedback method is illustrated. In the existing IA literature, the design of feedback method is not explicitly addressed. Existing works commonly assume CSI feedback from each receiver to all its interferers, corresponding to the full-feedback method illustrated in FIG. 3. For such a conventional method, each receiver transmits the CSI{H^([mk])}_(k=1) ^(K) to each of its K−1 transmitters and the resultant sum overhead is given in the following lemma.

Lemma 2. The sum overhead of the full-feedback method is given as below equation.

N _(FF) =K ²(K−1)M ²   [equation 10]

Each receiver feeds back K×M² nonzero coefficients to K−1 interferers. Since K receivers feed back CSI, total network overhead comprises K²(K−1)M².

From the above result, the overhead N_(FF) increases approximately as K³M² whereas the network throughput grows linearly with K. Thus the network overhead is potentially a limiting factor of the network throughput. By comparing proposition 1 and lemma 2, with respect to the full-feedback method, the CSI exchange method requires much less sum overhead for achieving IA, namely on the order of KM².

In the CSI exchange method described above, v^([1]) is solved by the eigenvalue problem that incorporates the channel matrices of all interfering links. However, it still requires a huge overhead in networks with many links or antennas. To suppress CSI overhead of v^([1]), we may apply the follow additional constraints indicated by a below equation.

span(H ^([i1]) v ^([1]))=span(H ^([i2]) v ^([2]))

span(H ^([i+11]) v ^([1]))=span(H ^([i+12]) v ^([2]))   [equation 11]

In equation 11, i is not 1 or 2. As v^([1]) and v^([2]) are aligned on same dimension at receiver i and i+1, v^([1]) is computed as the eigenvalue problem of (H^([i1]))⁻¹H^([i2])(H^([i+12]))⁻¹H^([i+11]) that consists of two interfering product matrices.

Applying the properties expressed in equation 11 at receive K−1 and K, it can be re-formulated IA condition under K=M+1 constraints as below equation.

$\begin{matrix} \begin{matrix} {{{span}\mspace{14mu} \left( {H^{\lbrack 12\rbrack}v^{\lbrack 2\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack 13\rbrack}v^{\lbrack 3\rbrack}} \right)}} & {{at}\mspace{14mu} {receiver}\mspace{14mu} 1} \\ {{{span}\mspace{14mu} \left( {H^{\lbrack 23\rbrack}v^{\lbrack 3\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack 24\rbrack}v^{\lbrack 4\rbrack}} \right)}} & {{at}\mspace{14mu} {receiver}\mspace{14mu} 2} \\ {\mspace{185mu} \vdots} & \; \\ {{{span}\mspace{11mu} \left( {H^{\lbrack{K - 11}\rbrack}v^{\lbrack 1\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack{K - 12}\rbrack}v^{\lbrack 2\rbrack}} \right)}} & {{{at}\mspace{14mu} {receiver}\mspace{14mu} K} - 1} \\ {{{span}\mspace{14mu} \left( {H^{\lbrack{K\; 1}\rbrack}v^{\lbrack 1\rbrack}} \right)} = {{span}\mspace{14mu} \left( {H^{\lbrack{K\; 2}\rbrack}v^{\lbrack 2\rbrack}} \right)}} & {{at}\mspace{14mu} {receiver}\mspace{14mu} K} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 12} \right\rbrack \end{matrix}$

It follows that v^([1]), v^([2]), . . . , v^([K]) can be selected as below equation.

$\begin{matrix} \begin{matrix} {v^{\lbrack 1\rbrack} = {{eigenvector}\mspace{14mu} {of}\mspace{14mu} \left( H^{\lbrack{K - 11}\rbrack} \right)^{- 1}{H^{\lbrack{K - 12}\rbrack}\left( H^{\lbrack{K\; 2}\rbrack} \right)}^{- 1}H^{\lbrack{K\; 1}\rbrack}}} \\ {v^{\lbrack 2\rbrack} = {\left( H^{\lbrack{K\; 2}\rbrack} \right)^{- 1}H^{\lbrack{K\; 1}\rbrack}v^{\lbrack 1\rbrack}}} \\ {v^{\lbrack 3\rbrack} = {\left( H^{\lbrack 13\rbrack} \right)^{- 1}H^{\lbrack 12\rbrack}v^{\lbrack 2\rbrack}}} \\ {\mspace{40mu} \vdots} \\ {v^{\lbrack K\rbrack} = {\left( H^{\lbrack{K - {2\; K}}\rbrack} \right)^{- 1}H^{\lbrack{K - {2\mspace{14mu} K} - 1}\rbrack}{v^{\lbrack{K - 1}\rbrack}.}}} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 13} \right\rbrack \end{matrix}$

Using the equation 13, the CSI exchange method can be modified. The modified CSI exchange method can be called a method 2 for a convenience. The method 2 comprises following procedures.

1. First, transmitters determine which interferers are aligned on the same subspace at each receiver. Then, each transmitter informs its corresponding receiver of the indices of two interferers to be aligned in the same direction (set-up).

2. Receiver K forwards the matrix (H^([K2]))⁻¹H^([K1]) to receiver K−1. Then, receiver K−1 computes v^([1]) as any eigenvector of (H^(K-11]))⁻¹H^([K-12])(H^([K2]))^(−1])H^([K1]) and feeds back it to the transmitter 1.

3. Transmitter 1 feeds forward v^([1]) to receiver K. Then, receiver K calculates v^([2]) and feeds back v^([2]) to transmitter 2.

4. Computation of v^([3]), . . . , v^([K])

Transmitter k−1 feeds forward v^([k−1]) to receiver k−2. Then, receiver k−2 calculates v^([k]) and feeds back v^([k]) to transmitter k−1. This process is performed for k=3 to K.

The corresponding sum overhead of modified CSI exchange method(method 2) is given in the following equation.

N _(MEX) =M ²+(2K−1)M   [equation 14]

Comparing the method 2 with the method 1, both methods show the same burden of overhead for the exchange of beamformers. However, the product channel matrix for v^([1]) in Modified CSI exchange method requires constant M² overhead in any K user case while overhead of v^([1]) in CSI exchange method increases with KM².

The CSI exchange methods(method 1, method 2) in previous description degrade the amount of network overhead compared with conventional full feedback method. However, it requires 2K−1 iterations for the exchange of beamformers in method 1 and 2. As the number of iterations is increased, a full DoF in K user channel cannot be achievable since it causes time delay that results in significant interference misalignment for fast fading. To compensate for these drawbacks, we suggest the star feedback method illustrated in FIG. 4.

FIG. 4 shows an example of star feedback method method(method 3).

Referring FIG. 4, wireless network comprises a CSI-base station, a plurality of transmitters, a plurality of receivers. The wireless network using the star feedback method comprises an agent, called the CSI base station (CSI-BS) which collects CSI from all receivers, computes all beamformers using IA condition in equation 7 or equation 13 and sends them back to corresponding transmitters. This method 3 is feasible since the computation of beamformers for IA is linked with each other and same interference matrices are commonly required for those beamformers. In large K, the star method allows much smaller delay compared with CSI exchange methods(method 1, method 2).

Star feedback method for IA in wireless network will be described.

1. Initialization: CSI-BS determines which interferers are aligned on the same dimension at each receiver. Then, it informs each receiver of the required channel information for computing IA.

2. Computation of v^([1]), . . . , v^([K]): The CSI-BS collects CSI from all receivers that comprises K×M² nonzero coefficients and computes beamformers v^([1]), . . . , v^([K]) with the collected set of CSI.

3. Broadcasting v^([1]), . . . , v^([K]): CSI-BS forwards v^([k]) to transmitter k for k=1, . . . , K, which requires M nonzero coefficient to each of K transmitters.

For the star feedback method in MIMO channel, the network overhead is given as below equation.

N _(SF)=(M ² |M)K   [equation 15]

Star feedback method requires only two time slots for computation of IA beamformers in any number of user K. Therefore, it is robust against channel variations due to the fast fading while CSI exchange methods are affected by 2K−1 slot delay for implementation. However, the network overhead of star feedback method is increased with KM² which is larger than 2KM in modified CSI exchange method. Also, star feedback method requires CSI-BS that connects all pairs of transmitter-receiver should be implemented as the additional costs.

The Network overhead of star feedback method can be quantified as below.

i) The overhead for initialization: Each receiver has K−1 interferers from other transmitters. Therefore, _(k−1)C₂ groups of aligned interferers exist at each receiver, where _(k−1)C₂=(K−1)(K−2)/2. To inform each receiver about the group of aligned interferers, total K log₂((K−1)(K−2)/2) bits are required for initial signaling.

ii) Feedback overhead for star feedback method: The CSI-BS collects CSI of product channels from all receivers that comprises KM² nonzero coefficients. After computing the precoding vectors, CSI-BS transmits a precoder of M nonzero coefficient to each of K transmitters. Therefore, (KM²+2M) nonzero coefficients are required for feedback in star feedback method.

The above result shows that the network overhead for the star feedback method is a linear function of K in contrast with the cubic function for conventional feedback method in MIMO channel. Thus the former method leads to a much slower increase of network overhead with K.

III. Effect of CSI Feedback Quantization

In the preceding description, the CSI feedback methods are designed on the assumption of perfect CSI exchange. However, CSI feedback from receiver to transmitter requires the channel quantization under the finite-rate feedback constraints in practical implementation. This quantized CSI causes the degradation of system performance due to the residual interference at each receiver. In this section, we characterize the throughput loss as the performance degradation in limited feedback. Furthermore, we derive an upper-bound of sum residual interference as the throughput loss and analyze it as a function of the number of feedback bits in given channel realization.

A. Throughput Loss in Limited Feedback

For the analytical simplicity, let define the throughput loss,

-   ΔR_(sum)

as below equation.

$\begin{matrix} \begin{matrix} {{\Delta \; R_{sum}}:={E_{H}\begin{bmatrix} {{\sum\limits_{k - 1}^{K}{\log_{2}\left( \frac{{Pd}_{kk}^{- \alpha}{{r^{{\lbrack k\rbrack}\dagger}H^{\lbrack{kk}\rbrack}v^{\lbrack k\rbrack}}}^{2}}{\sigma^{2}} \right)}} -} \\ {\sum\limits_{k - 1}^{K}{\log_{2}\left( \frac{{Pd}_{kk}^{- \alpha}{{r^{{\lbrack k\rbrack}\dagger}H^{\lbrack{kk}\rbrack}v^{\lbrack k\rbrack}}}^{2}}{{\hat{I}}^{\lbrack k\rbrack} + \sigma^{2}} \right)}} \end{bmatrix}}} \\ {= {{E_{H}\left\lbrack {\sum\limits_{k = 1}^{K}{\log_{2}\left( {\hat{I}}^{\lbrack k\rbrack} \right)}} \right\rbrack}\overset{(a)}{\leq}{E_{H}\left\lbrack {K\; {\log_{2}\left( {\frac{1}{K}{\sum\limits_{k = 1}^{K}{\hat{I}}^{\lceil k\rceil}}} \right)}} \right\rbrack}}} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 16} \right\rbrack \end{matrix}$

In equation 16,

-   {circumflex over (v)}^([k])

and

-   {circumflex over (r)}^([k])

are the transmit beamformer and receive filter based on the quantized CSI and ‘(a)’ follows from the characteristic of concave function log(x), denoting

${\hat{I}}^{\lbrack k\rbrack} = {\sum\limits_{{j - 1},{j \neq k}}^{K}{\frac{P}{d_{kj}^{\alpha}}{{{\hat{r}}^{{\lbrack k\rbrack}\dagger}H^{\lbrack{kj}\rbrack}{\hat{v}}^{\lbrack j\rbrack}}}^{2}}}$

.

Applying Jensen's inequality, the throughout loss is upper-bounded by below equation.

$\begin{matrix} {{\Delta \; R_{sum}} \leq {K\; {\log_{2}\left( {\frac{1}{K}{E_{H}\left\lbrack {\sum\limits_{k = 1}^{K}{\hat{I}}^{\lbrack k\rbrack}} \right\rbrack}} \right)}}} & \left\lbrack {{equation}\mspace{14mu} 17} \right\rbrack \end{matrix}$

In equation 17,

-   ΔR_(sum)

is significantly affected by the sum residual interference

${\hat{I}}^{sum} - {\sum\limits_{k = 1}^{K}{{\hat{I}}^{\lbrack k\rbrack}.}}$

. Therefore, the minimization of sum rate loss is equivalent to the minimization of sum residual interference caused by limited feedback bit constraints.

B. Residual Interference in the Proposed CSI Feedback Methods

Prior to deriving the bound of sum residual interference in proposed CSI feedback methods, we quantify the quantization error with RVQ using the distortion measure. Let denote h:=vec(H)/∥H∥,

-   ĥ:= -   vec(Ĥ)/∥Ĥ∥_(F)     and the phase rotation, -   e^(jφ):=ĥ^(|)h/|ĥ^(|)h     , where -   H, Ĥ ⊂ C^(N×M)     .

Then, h can be expressed by below equation.

h e^(jφ)ĥ+Δh   [equation 18]

In equation 18,

-   Δh

represents the difference between h and

-   e^(jφ)ĥ

. H is follows below equation.

-   ΔH

is defined such that

-   vec(ΔH)−Δh

H=e ^(jφ) Ĥ|ΔH   [equation 19]

Lemma 3. The expected squared norm of the random matrix

-   Δh

is bounded as below equation.

$\begin{matrix} {{E\left\lbrack {{\Delta \; H}}_{F}^{2} \right\rbrack} \leq {2{{\overset{\_}{\Gamma}({MN})} \cdot 2^{- \frac{B}{{MN} - 1}}}}} & \left\lbrack {{equation}\mspace{14mu} 20} \right\rbrack \end{matrix}$

In equation 20, B denotes quantization bit for H and

${\overset{\_}{\Gamma}({MN})} = \frac{2\; {\Gamma \left( \frac{1}{{MN} - 1} \right)}}{{MN} - 1}$

.

Using properties of lemma 3, we rewrite equation 18 and equation 19 as below equation.

h=e ^(jφ) ĥ+σ _(Δh) Δ{tilde over (h)}

H=e ^(jφ) Ĥ+σ _(Δh) Δ{tilde over (H)},   [equation 21]

In equation 21,

${{\Delta \; h} - {\sigma_{\Delta \; h}\Delta \; \overset{\sim}{h}}},{{E{{{\Delta \; \overset{\sim}{h}}}^{2}}} - 1},{{E{{{\Delta \overset{\sim}{H}}}_{F}^{2}}} - 1}$ and $\sigma_{\Delta \; h}^{2} \leq {{\overset{\_}{\Gamma}({MN})} \cdot 2^{- \frac{B}{{MN} - 1}}}$

.

Also, we derive the error bound of eigenvector of the quantized matrix, which is applied to analyze the sensitivity towards quantization error on the initialization of v^([1]). Based on the modeling of quantization error of H in equation 22, we derive the eigenvector of

-   Ĥ

using the perturbation theory in following lemma.

Lemma 4. For a large B, the m-th eigenvector

-   {circumflex over (v)}_(m)

of

-   Ĥ

is given as below equation.

[equation 22]

${\hat{v}}_{m} = {^{{- j}\; \phi}\left( {v_{m} - {\sigma_{\Delta \; h}{\sum\limits_{{k = 1},{k \neq m}}^{M}{\frac{v_{k}^{*}\Delta \; \overset{\sim}{H}v_{m}}{\lambda_{m} - \lambda_{k}}v_{k}}}}} \right)}$

In equation 22, v_(m) and λ_(m) are the m-th eigenvector and eigenvalue of H.

1) Modified CSI exchange method: As the modified CSI exchange method requires smaller sum overhead than that of CSI exchange method, we derive the upper bound of residual interference in modified CSI exchange method that consists of two types of feedback channel links: i) Feedback of the channel matrix for the initial v^([1]) and ii) Sequential exchange of the quantized beamformer between transmitter and receiver.

For the initialization of v^([1]), the K-th receiver transmits the product channel matrix H_(eff) ^(|K|) to the (K−1)^(th) receiver for the computation of v^(|1|) which is designed as the eigenvector of H_(eff) ^([K−1])H_(eff) ^([K]), where

$H_{eff}^{\lbrack{K - 1}\rbrack}:=\frac{\left( H^{\lceil{K - 11}\rceil} \right)^{- 1}H^{\lceil{K - 12}\rceil}}{{{\left( H^{\lbrack{K - 11}\rbrack} \right)^{- 1}H^{\lbrack{K - 12}\rbrack}}}_{F}}$ and $H_{eff}^{\lbrack K\rbrack}:{- \frac{\left( H^{\lbrack{K\; 2}\rbrack} \right)^{- 1}H^{\lbrack{K\; 1}\rbrack}}{{{\left( H^{\lbrack{K\; 2}\rbrack} \right)^{1}H^{\lbrack{K\; 1}\rbrack}}}_{F}}}$

.

Assuming that RVQ is applied and the quantized matrix

-   Ĥ_(eff) ^([K])     is transmitted through B_(initial) bits feedback channel, the     quantized channel matrix is expressed as below equation.

Ĥ_(eff) ^([K]) =e ^(−jφinitial)(H _(eff) ^([K])−σ_(initial) ΔĤ _(eff) ^([K]))   [equation 23]

In equation 23,

$\sigma_{initial}^{2} \leq {{\overset{\_}{\Gamma}\left( M^{2} \right)}2^{- \frac{R_{initial}}{M^{2} - 1}}}$ and E[Δ Ĥ_(eff)^([K])_(F)²] − 1

. Then, K−1-th receiver computes the quantized beamformer

-   {circumflex over (v)}^([1])

of

-   H_(eff) ^([K−1])Ĥ_(eff) ^([K])

as below equation.

{circumflex over (v)} ^([1]) =e ^(−jφinitial)(v ^([1]) −Δv ^([1]))   [equation 24]

In equation 24, the quantization error

-   Δv^([1])

is expressed by below equation.

$\begin{matrix} {{\Delta \; v^{\lfloor 1\rfloor}} = {\sigma_{initial}{\sum\limits_{{k = 1},{k \neq m}}^{M}{\frac{v_{k}^{*}H_{eff}^{\lbrack{K - 1}\rbrack}\Delta \; {\hat{H}}_{eff}^{\lbrack K\rbrack}v_{m}}{\lambda_{m} - \lambda_{k}}v_{k}}}}} & \left\lbrack {{equation}\mspace{14mu} 25} \right\rbrack \end{matrix}$

In equation 25, v_(m) and λ_(i) are the m-th eigenvector and eigenvalue of H_(eff) ^([K−1])H_(eff) ^([K]).

Since the magnitude of quantization error

-   ∥Δv^([1])|²

is represented as below equation, the quantization error of limited feedback is affected by exponential function of B_(initial) and the distribution of eigenvalues |λ_(m)−λ_(k)|.

$\begin{matrix} {{{\Delta \; v^{\lceil 1\rceil}}}^{2} \propto {\sum\limits_{{k = 1},{k \neq m}}^{M}\frac{\sigma_{initial}^{2}}{{{\lambda_{m} - \lambda_{k}}}^{2}}}} & \left\lbrack {{equation}\mspace{14mu} 26} \right\rbrack \end{matrix}$

Secondly, the effect of quantization error due to the exchange of beamformers between transmitters and receivers is described. On the assumption of perfect CSI of H_(eff) ^([K]) at receiver K−1, it is considered the limited feedback links from receiver to transmitter, where B_(k) bits are allocated to quantize

-   {circumflex over (v)}^([k])     . From modified CSI exchange method(method 2), receiver K−1 computes     v^([1]) and quantize it to -   {circumflex over (v)}^([1])     with RVQ and feeds back to transmitter 1 through B₁ feedback     channel. The quantized beamformer -   {circumflex over (v)}^([1])     is represented as below equation.

{circumflex over (v)} ^([1]) =e ^(−jφ) ^(K−1) (v ^([1])−σ₁ Δv ^([1]))   [equation 27]

In equation 27,

$\sigma_{1}^{2} - {{\overset{\_}{\Gamma}(M)}2^{- \frac{B_{1}}{M - 1}}\mspace{14mu} {and}}$ E[Δ v^([1])²] = 1

.

Following the procedure of method 2, transmitter 1 forwards

-   {circumflex over (v)}^([1])

to receiver K. The receiver K designs

-   {dot over (v)}^([2])

on the subspace of

-   H_(eff) ^([K]){circumflex over (v)}^([1])

as below equation.

$\begin{matrix} \begin{matrix} {{\overset{.}{v}}^{\lbrack 2\rbrack} = {{H_{eff}^{\lbrack K\rbrack}{\hat{v}}^{\lbrack 1\rbrack}} - {^{{- j}\; \phi_{1}}\left( {{H_{eff}^{\lbrack K\rbrack}v^{\lbrack 1\rbrack}} - {\sigma_{1}H_{eff}^{\lbrack K\rbrack}\Delta \; v^{\lbrack 1\rbrack}}} \right)}}} \\ {= {^{- {j\phi}_{1}}\left( {v^{\lbrack 2\rbrack} - {\sigma_{1}H_{eff}^{\lbrack K\rbrack}\Delta \; v^{\lbrack 1\rbrack}}} \right)}} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 28} \right\rbrack \end{matrix}$

Then, receiver K quantizes

-   {dot over (v)}^([2])

is quantized to

-   {circumflex over (v)}^([2])

with B₂ bit quantization level and informed to transmitter 2. The quantized precoder

-   {circumflex over (v)}^([2])

is represented as below equation.

{circumflex over (v)} ^([2]) =e ^(−jφ) ² ({dot over (v)} ^([2])−σ₂ Δv ^([2]))   [equation 29]

In equation 29,

$\sigma_{2}^{2} = {{\overset{\_}{\Gamma}(M)}2^{- \frac{B_{2}}{M - 1}}}$ and E[Δ v^([2])²] − 1

.

Likewise, the other beamformers

-   {dot over (v)}^([3]), {dot over (v)}^([4]), . . . , and {dot over     (v)}^([K])

are designed at receiver 1, 2, . . . , and K−2 and their quantized beamformers

-   {circumflex over (v)}^([3]), {circumflex over (v)}^([4]), . . . ,     and {circumflex over (v)}^([K])

are fed back to their corresponding transmitters, where

-   {circumflex over (v)}^([k])

is modeled as below equation.

{circumflex over (v)} ^([k]) =e ^(−jφ) ^(k) ({dot over (v)} ^([k])−σ_(k) Δv ^([k]))   [equation 30]

In equation 30,

-   σ_(k) ²=2⁻ _(M−1) ^(B) ^(k)

and

-   E[∥Δv^([k])∥²]−1

, for k=3, 4, . . . , K.

Using equation 28, 29 and 30, the sum residual interference affected by quantization error can be analyzed. To analyze the residual interference at each receiver, it is assumed that each receiver designs a zero-forcing receiver with a full knowledge of

{{circumflex over (v)} ^([k]): 1≦k≦K}

, which cancels M−1 dimensional interferers. Then, the upper-bound of expected sum residual interference is expressed as a function of the feedback bits, the eigenvalue of fading channel and distance between the pairs of transmitter-receiver is suggested as below.

Proposition 3. In modified CSI feedback method, the upper-bound of expected residual interference at each receiver is represented as below equation in given feedback bits {B_(k)}_(k=1) ^(K) and channel realization {H^([jk])}_(j,k=1) ^(K).

$\begin{matrix} {\left\{ \begin{matrix} {{{E\left\lbrack {\hat{I}}^{\lbrack k\rbrack} \right\rbrack} \leq {{Pd}_{{kk} + 2}^{- \alpha}\sigma_{k + 2}^{2}\lambda_{\max}^{\lbrack{{kk}|2}\rbrack}}},} & {{{{for}\mspace{14mu} k} = 1},\ldots \mspace{14mu},{K - 2}} \\ {{E\left\lceil {\hat{I}}^{\lbrack{K - 1}\rbrack} \right\rceil} \leq {{Pd}_{K - 11}^{- \alpha}\begin{matrix} \left( {{\sigma_{2}^{2}\lambda_{\max}^{\lbrack{K - 12}\rbrack}} +} \right. \\ \left. {{\sigma_{1}^{2}\lambda_{\max}^{\lbrack{K - 12}\rbrack}} + {\sigma_{1}^{2}\lambda_{\max}^{\lbrack{K - 11}\rbrack}}} \right) \end{matrix}}} & \; \\ {{E\left\lbrack {\hat{I}}^{\lbrack K\rbrack} \right\rbrack} \leq {{Pd}_{K\; 2}^{- \alpha}\sigma_{2}^{2}\lambda_{\max}^{\lbrack{K\; 2}\rbrack}}} & \; \end{matrix} \right.} & \left\lbrack {{equation}\mspace{14mu} 31} \right\rbrack \end{matrix}$

2) Star feedback method: In star feedback method, it is assumed that all receivers are connected to CSI-BS with high capacity backhaul links, such as cooperative multicell networks. Then, CSI-BS is allowed to acquire full knowledge of CSI estimated at each receiver. Based on equation 13, CSI-BS computes v^([1]), v^([2]), . . . , v^([K]) and forwards them to the corresponding transmitters. In this section, we consider the B_(k) feedback bits constraint from CSI-BS to transmitter k and v^([k]) is quantized as below equation.

{circumflex over (v)} ^([k]) =e ^(−jφ) ^(k) (v ^([k])−σ_(k) Δv ^([k]))   [equation 32]

In equation 32,

$\sigma_{k}^{2} = {{\overset{\_}{\Gamma}(M)}2^{- \frac{B_{k}}{M - 1}}}$ and E[Δ v^([k])²] = 1

.

Given the quantized beamformer, each transmitter sends a data stream that causes the residual interference at the receiver. Following proposition provides the upper-bound of expected residual interference at each receiver that consists of the feedback bits, the eigenvalue of fading channel and distance between the pairs of transmitter-receiver.

Proposition 4. In star feedback method, the upper-bound of expected residual interference at each receiver is represented in given feedback bits {B_(k)}_(k=1) ^(K) and channel realization {H^([jk])}_(j,k=1) ^(K)as below equation.

$\begin{matrix} {\left\{ \begin{matrix} \begin{matrix} {{E\left\lbrack {\hat{I}}^{\lbrack k\rbrack} \right\rbrack} \leq {{{Pd}_{{kk} + 2}^{- \alpha}\sigma_{k + 1}^{2}\lambda_{\max}^{\lbrack{{kk} + 1}\rbrack}} +}} \\ {{Pd}_{{kk} + 2}^{- \alpha}\sigma_{k + 2}^{2}\lambda_{\max}^{\lbrack{{kk} + 2}\rbrack}} \end{matrix} & {{{{for}\mspace{14mu} k} = 1},\ldots \mspace{14mu},{K - 2}} \\ \begin{matrix} {{E\left\lbrack {\hat{I}}^{\lbrack{K - 1}\rbrack} \right\rbrack} \leq {{{Pd}_{K - 11}^{- \alpha}\sigma_{1}^{2}\lambda_{\max}^{{K - 11}}} +}} \\ {{Pd}_{K - 11}^{- \alpha}\sigma_{2}^{2}\lambda_{\max}^{{K - 12}}} \end{matrix} & \; \\ {{E\left\lbrack {\hat{I}}^{\lbrack K\rbrack} \right\rbrack} \leq {{{Pd}_{K\; 2}^{- \alpha}\sigma_{1}^{2}\lambda_{\max}^{\lbrack{K\; 1}\rbrack}} + {{Pd}_{K\; 2}^{- \alpha}\sigma_{2}^{2}\lambda_{\max}^{\lbrack{K\; 2}\rbrack}}}} & \; \end{matrix} \right.} & \left\lbrack {{equation}\mspace{14mu} 33} \right\rbrack \end{matrix}$

IV. Dynamic Feedback Bit Allocation Methods for CSI Feedback Methods

The system performance of IA is significantly affected by the number of feedback bits that induces residual interference in a finite rate feedback channel. The dynamic feedback bit allocation strategies that minimize the throughput loss due to the quantization error in given sum feedback bits constraint is explained. Furthermore, It is provided the required number of feedback bits achieving a full network DoF in each CSI feedback methods.

A. Dynamic Bit Allocation for Minimizing Throughput Loss

Consider total B_(tot) bits are given for the feedback framework in the network model. Since the throughput loss is bounded by the expected sum residual interference, we can formulate the dynamic bit allocation problem for minimizing throughput loss as follows equation.

$\begin{matrix} {{\min {\sum\limits_{k = 1}^{K}{E\left\lbrack {\hat{I}}^{\lbrack k\rbrack} \right\rbrack}}}{{s.t.{\sum\limits_{k - 1}^{K}B_{k}}} \leq B_{tot}}} & \left\lbrack {{equation}\mspace{14mu} 34} \right\rbrack \end{matrix}$

Using proposition 3 and proposition 4, equation 34 can be transformed to convex optimization problem with variables {B_(k)}_(k=1) ^(K) expressed by below equation.

$\begin{matrix} {{\min {\sum\limits_{k = 1}^{K}{a_{k}2^{- \frac{B_{k}}{M - 1}}}}}{{s.t.{\sum\limits_{k - 1}^{K}B_{k}}} \leq B_{tot}}} & \left\lbrack {{equation}\mspace{14mu} 35} \right\rbrack \end{matrix}$

Here, we define a_(k) in modified CSI exchange method(method 2) as below equation.

$\begin{matrix} {{a_{k} = \left\{ \begin{matrix} \begin{matrix} {a_{1} - {{\overset{\_}{\Gamma}(M)} \cdot {P\left( {{d_{K - 11}^{- \alpha}\lambda_{\max}^{\lbrack{K - 12}\rbrack}} +} \right.}}} \\ \left. {d_{K - 11}^{- \alpha}\lambda_{\max}^{\lbrack{K - 11}\rbrack}} \right) \end{matrix} & \; \\ {a_{2} = {{\overset{\_}{\Gamma}(M)} \cdot {P\left( {{d_{K\; 2}^{- \alpha}\lambda_{\max}^{\lbrack{K2}\rbrack}} + {d_{K - 11}^{- \alpha}\lambda_{\max}^{\lbrack{K - 12}\rbrack}}} \right)}}} & \; \\ {a_{k} = {{{\overset{\_}{\Gamma}(M)} \cdot {Pd}_{k - {2\; k}}^{- \alpha}}\lambda_{\max}^{\lbrack{k - {2\; k}}\rbrack}}} & {{{\forall k} = 3},\ldots \mspace{14mu},K} \end{matrix} \right.}} & \left\lbrack {{equation}\mspace{14mu} 36} \right\rbrack \end{matrix}$

And, a_(k) in star feedback method as below equation.

$\begin{matrix} {{a_{k} = \left\{ \begin{matrix} {a_{1} = {{\Gamma (M)} \cdot {P\left( {{d_{K\; 2}^{- \alpha}\lambda_{\max}^{\lbrack{K\; 1}\rbrack}} + {d_{K - 11}^{- \alpha}\lambda_{\max}^{\lbrack{K - 11}\rbrack}}} \right)}}} & \; \\ {a_{2} = {{\overset{\_}{\Gamma}(M)} \cdot {P\left( {{d_{13}^{- \alpha}\lambda_{\max}^{\lbrack 12\rbrack}} + {d_{K\; 2}^{- \alpha}\lambda_{\max}^{\lbrack{K\; 2}\rbrack}}} \right)}}} & \; \\ {a_{k} = \begin{matrix} {{\overset{\_}{\Gamma}(M)} \cdot {P\left( {{d_{k - {1k} + 1}^{- \alpha}\lambda_{\max}^{\lbrack{k - {1k}}\rbrack}} +} \right.}} \\ {\left. {d_{k - {2\; k}}^{{- \alpha}|}\lambda^{\lbrack{k - {2\; k}}\rbrack}} \right),} \end{matrix}} & {{k = 3},\ldots \mspace{14mu},{K - 1}} \\ {a_{K} = \begin{matrix} {{\overset{\_}{\Gamma}(M)} \cdot {P\left( {{d_{K - 11}^{- \alpha}\lambda_{\max}^{\lbrack{K - {1K}}\rbrack}} +} \right.}} \\ \left. {d_{K - {2\; K}}^{- \alpha}\lambda_{\max}^{\lbrack{K - {2\; K}}\rbrack}} \right) \end{matrix}} & \; \end{matrix} \right.}} & \left\lbrack {{equation}\mspace{14mu} 37} \right\rbrack \end{matrix}$

Consider the objective function in equation 35. By forming the lagrangian and taking derivative with respect to B_(k), it can be expressed by below equation.

$\begin{matrix} {{L = {{\sum\limits_{k \in U}{a_{k}2^{- \frac{B_{k}}{M - 1}}}} + {v\left( {{\sum\limits_{k \in U}B_{k}} - B_{tot}} \right)}}}{\frac{\partial L}{\partial B_{k}} = {{{{- 2^{- \frac{B_{k}}{M - 1}}}\ln \; 2\frac{a_{k}}{M - 1}} + v} = 0}}} & \left\lbrack {{equation}\mspace{14mu} 38} \right\rbrack \end{matrix}$

In equation 38, v is the Lagrange multiplier and U is the set of feedback link U={1, . . . K}. Therefore, we obtain B_(k) as below equation.

$\begin{matrix} {{B_{k}\left( {M - 1} \right)} \cdot {\log_{2}\left( \frac{\mu \; a_{k}}{M - 1} \right)}} & \left\lbrack {{equation}\mspace{14mu} 39} \right\rbrack \end{matrix}$

and B_(k) satisfies the following constraint equation where

$\begin{matrix} {\mu = {{\frac{\ln \; 2}{v}.{\sum\limits_{k \in U}{\left( {M - 1} \right) \cdot {\log_{2}\left( \frac{\mu \; a_{k}}{M - 1} \right)}}}} = B_{tot}}} & \left\lbrack {{equation}\mspace{14mu} 40} \right\rbrack \end{matrix}$

Combining equation 39 and 40 with B_(k)≧0, the number of optimal feedback bit is obtained as below equation.

$\begin{matrix} {{B_{k}^{*} = {\frac{1}{U}\left( {\gamma - {\left( {M - 1} \right) \cdot {U} \cdot {\log_{2}\left( \frac{M - 1}{a_{k}} \right)}}} \right)^{+}}}{\mu = \left( {2^{B_{tot}}{\prod\limits_{k \in U}\; \left( \frac{M - 1}{a_{k}} \right)^{M - 1}}} \right)^{\frac{1}{{({M - 1})} \cdot {U}}}}{{{In}\mspace{14mu} {equation}\mspace{14mu} 41},{\gamma - B_{tot} + {\sum\limits_{k \in U}{\left( {M - 1} \right) \cdot {\log_{2}\left( \frac{M - 1}{a_{k}} \right)}}}}}} & \left\lbrack {{equation}\mspace{14mu} 41} \right\rbrack \end{matrix}$

, |U| is the cardinality of U and

$(a)^{+}\left\{ \begin{matrix} a & {{{if}\mspace{14mu} a} \geq 0} \\ 0 & {{{if}\mspace{14mu} a} < 0} \end{matrix} \right.$

The solution of equation 41 is found iteratively through the waterfilling algorithm, which is described as bellow.

Waterfilling algorithm.

i=0; U={1, . . . , K};

while i=0 do

Determine the water-level

$\gamma = {B_{tot} + {\sum\limits_{k \in U}{\left( {M - 1} \right) \cdot {\log_{2}\left( \frac{M - 1}{a_{k}} \right)}}}}$

.

Choose the user set

$\overset{\_}{k} = {\arg \; \max \left\{ {\frac{M - 1}{a_{k}}:{k \in U}} \right\}}$

.

if

${\gamma - {\left( {M - 1} \right) \cdot {U} \cdot {\log_{2}\left( \frac{M - 1}{a_{\overset{\_}{k}}} \right)}}} \geq 0$

then optimal bit allocation

-   B*_(k)

in U is determined by equation 43.

i=i+1;

else Let define

-   U={U except for k} and B* _(k) =0

.

From the waterfilling algorithm, we obtain {B_(k)*: k ∈ U}. However, B_(k)* should become integer so that it is determined the optimal bit allocation

-   B*_(k)

as below equation.

B*_(k)[equation 42]

In equation 42,

-   [x]

is the largest integer not greater than x.

B. Scaling Law of Total Feedback Bits

In perfect CSI assumption, each pair of transmitter-receiver link obtains the interference-free link for its desired data stream. However, misaligned beamformers due to the quantization error destroy the linear scaling gain of sum capacity at high SNR regime. In this section, we analyze the total number of feedback bits that achieve a full network DoF K in the proposed CSI feedback methods. The required sum feedback bits are formulated as the function of the channel gains, the number of antennas and SNR that maintain the constant sum rate loss over the whole SNR regimes.

As P goes to infinity, the network DoF in K user interference channel achieves K with constant value of

$\sum\limits_{k = 1}^{K}{\log_{2}\left( {\hat{I}}^{\lbrack k\rbrack} \right)}$

according to below equation.

$\begin{matrix} \begin{matrix} {{DoF} = {\lim\limits_{P\rightarrow\infty}\frac{R_{sum}^{limited}}{\log_{2}P}}} \\ {= {\lim\limits_{P\rightarrow\infty}\frac{{\sum\limits_{k = 1}^{K}{\log_{2}\left( {\frac{P}{d_{kk}^{\alpha}}{{{\hat{r}}^{\lbrack k\rbrack}H^{\lbrack{kk}\rbrack}{\hat{v}}^{\lbrack k\rbrack}}}^{2}} \right)}} - {\sum\limits_{i = 1}^{K}{\log_{2}\left( {\hat{I}}^{\lbrack k\rbrack} \right)}}}{\log_{2}P}}} \\ {= {{\lim\limits_{P\rightarrow\infty}\frac{\sum\limits_{k = 1}^{K}{\log_{2}\left( {\begin{matrix} P \\ d_{kk}^{\alpha} \end{matrix}{{{\hat{r}}^{\lbrack k\rbrack}H^{\lbrack{kk}\rbrack}{\hat{v}}^{\lbrack k\rbrack}}}^{2}} \right)}}{\log_{2}P}} -}} \\ {{\lim\limits_{P\rightarrow\infty}\frac{\sum\limits_{k = 1}^{K}{\log_{2}\left( {\hat{I}}^{\lbrack k\rbrack} \right)}}{\log_{2}P}}} \\ {= {K - {\lim\limits_{P\rightarrow\infty}\frac{\log_{2}\left( {\prod\limits_{k = 1}^{K}\; {\hat{I}}^{\lbrack k\rbrack}} \right)}{\log_{2}P}} - K}} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 43} \right\rbrack \end{matrix}$

Therefore, it can be formulated the sum residual interference as an exponential function of B_(k) as below equation.

$\begin{matrix} \begin{matrix} {{\sum\limits_{k = 1}^{K}{\log_{2}\left( {\hat{I}}^{\lbrack k\rbrack} \right)}} = {\log_{2}\left( {\prod\limits_{k = 1}^{K}\; {\hat{I}}^{\lbrack k\rbrack}} \right)}} \\ {= {\log_{2}\left( {\prod\limits_{k = 1}^{K}\; {a_{k}2^{- \frac{B_{k}}{M - 1}}}} \right)}} \\ {= c} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 44} \right\rbrack \end{matrix}$

Equation 44 yields an equation 45.

$\begin{matrix} \begin{matrix} {B_{tot}^{*} = {\sum\limits_{k = 1}^{K}B_{k}}} \\ {= {\left( {M - 1} \right) \cdot \left( {{\sum\limits_{k}^{K}{\log_{2}a_{k}}} - {\log_{2}c}} \right)}} \\ {= {{{K \cdot \left( {M - 1} \right) \cdot \log_{2}}P} + {\left( {M - 1} \right) \cdot \left( {{\sum\limits_{k = 1}^{K}{\log_{2}{\hat{a}}_{k}}} - c} \right)}}} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 45} \right\rbrack \end{matrix}$

In equation 45, c is constant and c>0 and

a _(k) =P·â _(k) , ∀k

.

Since B_(tot)* is the integer number, we obtain the required feedback bits

-   B*_(tot)

for achieving full DoF as below equation.

B* _(tot)nint(B* _(tot))   [equation 46]

In equation 46, nint(x) is the nearest integer function of x.

C. Implementation of Feedback Bits Controller

As we derived in equation 41 and 45, the computation of

-   { B*_(K)}_(k=1) ^(K)

and

-   B*_(tot)

requires the set of channel gain {a_(k)}_(k=1) ^(K) that consist of the path-loss and small-scale fading gain from all receivers. Therefore, the optimal bit allocation strategy requires the centralized bit controller that gathers full knowledge of {a_(k)}_(k=1) ^(K) and computes the optimal set of feedback bits. The centralized bit controller can be feasible in star feedback method since it has a CSI-BS connected with all receivers through the backhaul links.

However, the receiver in CSI exchange method only feeds back CSI to corresponding transmitter, implemented by distributed feedforward/feedback channel links. Therefore, the additional bit controller is required to allocate optimal feedback bits in CSI exchange method. Moreover, {a_(k)}_(k=1) ^(K) includes the gain of short-term fading so that CSI of all receivers should be collected to central controller over every transmission period, which requires a large overhead of CSI exchange and causes delay that results in significant interference misalignment in fast channel variation environments.

1) Path-loss based bit allocation method: To reduce the burden of frequent exchange of channel gains for bit allocation, we average {a_(k)}_(k=1) ^(K) over the short-term fading {H^([ij])}_(i,j=1) ^(K). Consider

-   E[λ_(max) ^([ij])]≦M²

and apply it to equation 36. E_(H)[a_(k)] in modified CSI exchange method is represented as below equation where the expected value of ak's consists of transmit power, the number of antennas and path-loss.

                                [equation  47] ${E_{H}\left\lceil a_{k} \right\rceil} = \left\{ \begin{matrix} {{E_{H}\left\lbrack a_{1} \right\rbrack} = {{{\overset{\_}{\Gamma}(M)} \cdot 2}\; {P \cdot M^{2} \cdot d_{K - 11}^{- \alpha}}}} \\ {{E_{H}\left\lbrack a_{2} \right\rbrack} = {{\overset{\_}{\Gamma}(M)} \cdot {PM}^{\; {2\; \cdot {({d_{K\; 2}^{\alpha} + d_{k - 11}^{- \alpha}})}}}}} \\ {{{E_{H}\left\lbrack a_{k} \right\rbrack} - {{\overset{\_}{\Gamma}(M)} \cdot {PM}^{\; 2} \cdot d_{k - {2\; k}}^{- \alpha}}},{\forall{k - 3}},\ldots \mspace{14mu},K} \end{matrix} \right.$

Applying equation 47 to 41 and 45, the dynamic bit allocation scheme can be implemented by the path-loss exchange between receivers. Since the path-loss shows a long-term variability compared with fast fading channel, the additional overhead for bit allocation is required over the much longer period than the bit allocation scheme in equation 41 and 45.

2) Distributed bit allocation scheme in CSI exchange method: We develop the distributed bit allocation scheme achieving DoF K in modified CSI exchange method, without centralized bit controller. For the distributed bit allocation, we replace the condition (equation 44) that satisfies DoF K as below equation.

$\begin{matrix} \begin{matrix} {{\log_{2}\left( {\hat{I}}^{\lbrack k\rbrack} \right)} = {\log_{2}\left( {a_{k}2^{- \frac{B_{k}}{M\mspace{14mu} 1}}} \right)}} \\ {{= \frac{c}{K}},{{\forall k} = 1},\ldots \mspace{14mu},K} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 48} \right\rbrack \end{matrix}$

Therefore, the required feedback bit Bk for k-th beamformer is derived as below equation where a_(k) is represented in equation 36.

$\begin{matrix} {B_{k} - {{\left( {M - 1} \right) \cdot \log_{2}}a_{k}} - {{\left( {M - 1} \right) \cdot \log_{2}}\frac{c}{K}}} & \left\lbrack {{equation}\mspace{14mu} 49} \right\rbrack \end{matrix}$

Combining equation 49 with 36, each receiver can compute the required feedback bits with its own channel knowledge except for receiver K. Receiver K requires additional knowledge of

-   d_(K−11) ^(−α)λ_(max) ^([K−12])

from receiver K−1 to calculate a₂. From equation 49, it is provided the design of modified CSI exchange method with bit allocation of achieving IA in distributed manners as below.

1. step 1; set-up: Receiver K forwards the matrix (H^([K2]))⁻¹H^([K1]) to receiver K−1.

2. step 2; computation of v^([1]): Receiver K−1 computes v^([1]) as any eigenvector of (H^([K−11]))⁻¹H^([K2]))⁻¹H^([K1]) and quantizes it to

-   {circumflex over (v)}^([1])

with B₁ computed by equation 49.

-   {dot over (v)}^([1])

is forwarded to transmitter 1. Also, receiver K−1 forwards

-   d_(K−1) ^(−α)λ_(max) ^([K−12])

to receiver K.

3. step 3; computation of v[2]: Transmitter 1 feeds forward

-   {circumflex over (v)}^([1])

to receiver K. Then, receiver K calculates

-   {dot over (v)}^([2])

and quantizes it to

-   {circumflex over (v)}^([2])

with B₂ computed by equation 49.

-   {circumflex over (v)}^([2])

is fed back to transmitter 2.

4. step 4; computation of v^([3]), . . . , v^([K])

For i=3: K, Transmitter i−1 forwards

-   {circumflex over (v)}^([i−1])

to receiver i−2. Then, receiver i−2 calculates

-   {dot over (v)}^([i])

and quantizes it to

-   {circumflex over (v)}^([i])

with B_(i) computed by equation 49.

-   {circumflex over (v)}^([i])

is fed back to transmitter i−1.

V. Successive CSI Exchange Method Based on Precoded RS

CSI exchange method in previous description can reduce the amount of network overhead for achieving IA. However, it basically assumes the feedforward/feedback channel links to exchange pre-determined beamforming vectors.

Considering precoded RS in LTE (long term evolution), the CSI exchange method can be slightly modified for the exchange of precoding vector (i.e. beamformer or beamforming vector). However, it still effectively scales down CSI overhead compared with conventional feedback method.

Consider K=M+1 user interference channel, where each transmitter and receiver are equipped with M antennas and DoF(d) for each user is d=1. Based on IA condition and precoding vector in equation 6 and equation 7, we firstly explain the procedure of CSI exchange method in feedforward/feedback framework for K=4. After that, we describe the procedure of CSI exchange method based on precoded RS, in detail.

1. Successive CSI exchange in method 1(e.g. K=4)

In K=4, IA condition is shown as below equation.

$\left. \begin{matrix} \begin{matrix} \begin{matrix} {{{span}\left( {H^{\lbrack 13\rbrack}V^{\lbrack 3\rbrack}} \right)} = {{{span}\left( {H^{\lbrack 14\rbrack}V^{\lbrack 4\rbrack}} \right)}\mspace{14mu} {At}\mspace{14mu} {receiver}\mspace{14mu} 1}} \\ {{{span}\left( {H^{\lbrack 21\rbrack}V^{\lbrack 1\rbrack}} \right)} = {{{span}\left( {H^{\lbrack 24\rbrack}V^{\lbrack 4\rbrack}} \right)}\mspace{14mu} {At}\mspace{14mu} {receiver}\mspace{14mu} 2}} \end{matrix} \\ {{{span}\left( {H^{\lbrack 31\rbrack}V^{\lbrack 1\rbrack}} \right)} = {{{span}\left( {H^{\lbrack 32\rbrack}V^{\lbrack 2\rbrack}} \right)}\mspace{14mu} {At}\mspace{14mu} {receiver}\mspace{14mu} 3}} \end{matrix} \\ {{{span}\left( {H^{\lbrack 42\rbrack}V^{\lbrack 2\rbrack}} \right)} = {{{span}\left( {H^{\lbrack 43\rbrack}V^{\lbrack 3\rbrack}} \right)}\mspace{14mu} {At}\mspace{14mu} {receiver}\mspace{14mu} 4}} \end{matrix}\rightarrow\begin{matrix} \begin{matrix} \begin{matrix} {{{span}\left( V^{\lbrack 1\rbrack} \right)} = {{span}\left( {\left( H^{\lbrack 31\rbrack} \right)^{- 1}H^{\lbrack 32\rbrack}V^{\lbrack 2\rbrack}} \right)}} \\ {{{span}\left( V^{\lbrack 2\rbrack} \right)} = {{span}\left( {\left( H^{\lbrack 42\rbrack} \right)^{- 1}H^{\lbrack 43\rbrack}V^{\lbrack 3\rbrack}} \right)}} \end{matrix} \\ {{{span}\left( V^{\lbrack 3\rbrack} \right)} = {{span}\left( {\left( H^{\lbrack 13\rbrack} \right)^{- 1}H^{\lbrack 14\rbrack}V^{\lbrack 4\rbrack}} \right)}} \end{matrix} \\ {{{span}\left( V^{\lbrack 4\rbrack} \right)} = {{span}\left( {\left( H^{\lbrack 24\rbrack} \right)^{- 1}H^{\lbrack 21\rbrack}V^{\lbrack 1\rbrack}} \right)}} \end{matrix} \right.$

Then, precoding vectors at transmitter 1, 2, 3 and 4 can be determined as below equation.

V ^([1])=eigen vector of (H ^([31]))⁻¹ H ^([32])(H ^([42]))⁻¹ H ^([43])(H ^([13]))⁻¹ H ^([14])(H ^([24]))⁻¹ H ^([21])

V ^([2])=(H ^([32]))⁻¹ H ^([31]) V ^([1])

V ^([3])=(H ^([43]))¹ H ^([42]) V ^([2])

V ^([4])=(H ^([14]))⁻¹ H ^([13]) V ^([3])  [equation 51]

In equation 50, the interference from transmitter 3 and 4 are on the same subspace at receiver 1. Likewise, the interference from transmitter 1 and 4, the interference from transmitter 1 and 2, and the interference from transmitter 2 and 3 are on the same subspace at receiver 2,3, and 4.

To inform the receiver that which interferers are aligned on the same dimension, CSI exchange method firstly requires initial signaling. This initial signaling is transmitted with a longer period than that of precoder update.

After the initial signaling indicated which interferers are aligned at each receiver, the receiver 1,2,3,4 feed back the selected interference channel matrices multiplied by the inverse of another interference channel matrices, {(H^([31]))⁻¹H^([32]), (H^([42]))⁻¹H^([43]), (H^([13]))⁻¹H^([14]), (H^([24]))⁻¹H^([21])} to transmitter 1. Based on these product channel matrices, transmitter 1 determines V^([1]) and forwards it to receiver 3. Then, receiver 3 determines V^([2]) using the channel information of (H^([32]))⁻¹H^([31]), and feeds it back to transmitter 2. With this iterative way, V^([3]) and V^([4]) are sequentially determined. This process is already described in previous part. However, it is slightly different in the point that V^([1]) is determined by transmitter 1 not a receiver 3.

FIG. 5 shows the procedure of CSI exchange method in case that K is 4.

Referring FIG. 5( a), the procedure of CSI exchange method comprises below steps.

step 1. Initial Signaling: Each transmitter(e.g. BS) reports which interferers are aligned on the same dimension to its receiver. (In K=4, two of four interferers are selected as the aligned interference.)

step 2. Determine initial vector V^([1]): After initialization, receiver 1,2,3,4 feed back the product channel matrices to transmitter 1, then transmitter 1 can determine V^([1]).

step 3. Computation of V^([2]) V^([3]) and V^([4]): Transmitter 1 feeds forward V^([1]) to receiver 3 and receiver 3 calculates V^([2]). After that, receiver 3 feeds back V^([2]) to transmitter 2. With the exchange of pre-determined precoding vector, the remained precoding vectors are sequentially computed.

Referring FIG. 5( b), the step 3 in the CSI exchange method is shown, where FF is feedfoward and FB is feedback links.

In a system using precoded RS such as a LTE system, each transmitter(e.g. BS) transmits the reference symbol with precoding vector. Therefore, the feedfoward signaling in successive CSI exchange can be modified compared with above CSI exchange method.

FIG. 6 shows the procedure of CSI exchange method based on precoded RS in case that K is 4.

Assume that i-th receiver perfectly estimates the channel matrices {H^([1i]),H^([2i]), . . . , H^([Ki])}. Then, precoding vectors V^([1]),V^([2]),V^([3]), and V^([4]) for IA are designed by equation 51 in K=4. To compute the precoding vectors, the procedure of CSI exchange method based on precoded RS is suggested as below.

Referring FIG. 6( a), the procedure of CSI exchange method based on precoded RS comprises below steps.

step 1. Initial signaling: Each BS(transmitter) reports which interferers are aligned on same dimension to its receiver. (The index of aligned interferers is forwarded to the receiver.)

step 2. Determine initial vector V^(|1|): After initialization, receiver 1,2,3,4 feed back the product channel matrices to transmitter 1, then transmitter 1 can determine V^([1]). Here, the product channel matrices comprises (H^([31]))⁻¹H^([32]), (H^([42]))⁻¹H^([43]), (H^([13]))⁻¹H^([14]) and (H^([24]))⁻¹H^([21]).

step 3. computation V^([2]), V^([3]) and V^([4]).

referring FIG. 6( b), the process of the step 3 includes below procedures.

Transmitter 1: After determining its precoder V^([1]), transmitter 1 sends the precoded RS to receiver 3.

Receiver 3: Receiver 3 estimates effective channel H_(eff) ^([31])=H^([31])V^([1]). Based on equation 51, receiver 3 computes V^(|2|) multiplying H_(eff) ^(|31|) with (H^(|32|))⁻¹. Then, receiver 3 sends V^([2]) to transmitter 2 through feedback channel.

Transmitter 2: Transmitter 2 sends the precoded RS based on V^([2]) to receiver 4.

Receiver 4: Receiver 4 estimates effective channel H_(eff) ^([42])=H^([42])V^([2]) and it computes V^([3]) multiplying H_(eff) ^([42]) with (H^([43]))⁻¹. Then, V^([4]) is reported to transmitter 3 through feedback channel.

Transmitter 3: Transmitter sends the precoded RS based on V^([3]) to receiver 1.

Receiver 1: Receiver 1 estimates effective channel H_(eff) ^([13])=H^([13])V^([3]) and receiver 1 computes V^([4]) multiplying H_(eff) ^([13]) with (H^([14 ])

)⁻¹. Then, V^([4]) is reported to transmitter 4 through feedback channel.

VI. Efficient Interference Alignment Method for 3 Cell MIMO Interference Channel with Limited Feedback.

There are known-closed form solutions for K user MIMO interference channel where K=3 with d=M/2 and K=M+1 with d=1. Here, d denotes a degree of freedom. In both case, each transmitter needs information of 2K product channel matrices for the initialization in equation 6, 7 since all precoders are coupled.

If quantized channel feedback is assumed, error of product-quantized matrices for IA solution is much larger than error of single channel matrix quantization.

As an example, consider K=3 and M=2 with d=1. The solution of V^([1]) is any eigenvector of (H^([31]))⁻¹H^([32])(H^([12]))⁻¹H^([23]))⁻¹H^([21]). If each receiver feeds back a product channel matrix for aligned interferences (e.g at receiver 3, feeding back H_(eff) ^([3])=(H^([32]))⁻¹H^([31]) to transmitter 1), the precoder at transmitter 1 is estimated as below equation.

$\begin{matrix} \begin{matrix} {{\hat{V}}^{\lbrack 1\rbrack} = {{eig}\begin{pmatrix} \left( {{\left( H^{\lbrack 31\rbrack} \right)^{- 1}H^{\lbrack 32\rbrack}} + E^{\lbrack 3\rbrack}} \right) \\ \left( {{\left( H^{\lbrack 12\rbrack} \right)^{- 1}H^{\lbrack 13\rbrack}} + E^{\lbrack 1\rbrack}} \right) \\ \left( {{\left( H^{\lbrack 23\rbrack} \right)^{- 1}H^{\lbrack 21\rbrack}} + E^{\lbrack 2\rbrack}} \right) \end{pmatrix}}} \\ {= {{eig}\begin{pmatrix} {\left( H^{\lbrack 31\rbrack} \right)^{- 1}{H^{\lbrack 32\rbrack}\left( H^{\lbrack 12\rbrack} \right)}^{- 1}} \\ {{{H^{\lbrack 13\rbrack}\left( H^{\lbrack 23\rbrack} \right)}^{- 1}H^{\lbrack 21\rbrack}} + E_{total}} \end{pmatrix}}} \\ {= {{eig}\left( {V^{\lbrack 1\rbrack} + E_{total}} \right)}} \end{matrix} & \left\lbrack {{equation}\mspace{14mu} 52} \right\rbrack \end{matrix}$

In equation 52, EN is channel quantization error at node n and E_(total) is all but V^([1]). On account of matrix multiplication, Frobenius norm of E_(total) is larger than it of E^([n]) since E_(total) is made up of summation and multiplication of E^([n]). We will refer to this as the quantized error amplification (QEA) in interference alignment system. In other words, to satisfy a given feedback CSI distortion level in interference alignment system, much more feedback bits are required than that of point to point network. Therefore, in limited feedback system, IA solution for achieving the optimal DOF has much degradation compared with perfect feedback. This demands the development of an efficient alignment method to avoid QEA.

Basically, the QEA is due to the chaining condition of IA solution. Main idea of this embodiment of the present invention is use of fallow spatial DOFs to cut the chaining condition. If a node does not use some spatial DOF, IA solution is detached to several independent equations.

For example, let consider K=3 user and M×M MIMO interference channel. It is assumed that transmitter 1 is the sacrifice node (any node can be the sacrifice node) which means the DOF for the transmitter 1(d₁) is M/2−α, where a is the fallow DOF, and other 2 nodes use d₂=d₃=M/2 DOF. Based on IA condition in equation 50, first M/2−α beams at transmitter 2, 3 have to be aligned with beams of transmitter 1 and last α beams of transmitter 2, 3 have to be aligned at receiver 1, which is shown as below equation.

span(H ^([32]) V _([1, . . . , M/2−α]) ^([2]))=span(H ^([31]) V _([1, . . . , M/2−α]) ^([1]))

span(H ^([23]) V _([1, . . . , M/2−α]) ^([3]))=span(H ^([21]) V _([1, . . . , M/2−α]) ^([1]))

span(H ^([13]) V _([M/2−α+1, . . . , M/2]) ^([3]))=span(H ^([12]) V _([M/2−α+1, . . . M/2]) ^([2]))   [equation 53]

Differently equation 50, equations included in the equation 53 are not coupled with each other. If transmitter 1 uses a predetermined precoder that is known to all receivers, first M/2−α beams at transmitter 2, 3 can be easily determined as below equation.

V_([1, . . . , M/2−α]) ^([2])

span((H^([32]))⁻¹ H^([31])V_([1, . . . , M/2−α]) ^([1]))

V_([1, . . . , M/2−α]) ^([3])

span((H^([23]))⁻¹ H^([21])V_([1, . . . , M/2−α]) ^([1]))   [equation 54]

In equation 54, V_([1, . . . , M/2−α]) ^([1]) is a M×(M/2−α) predetermined precoder at node 1 which is known at all receivers. Similarly, using predetermined vectors for last α beams at transmitter 2, last α beams at transmitter 3 can be determined as below equation.

V_([M/2−α+1, . . . , M/2]) ^([3])

span((H^([13]))⁻¹ H^([12])W_([M/2−α+1, . . . , M/2]) ^([2]))   [equation 55]

In equation 55, W_([M/2−α+1, . . . , M/2]) ^([2]) is a (M×α) predetermined matrix at node 2 which can be known to all receivers. Then, final precoder can be determined by using QR decomposition since beamforming vectors at transmitter 2,3 are satisfied with orthogonal property, which is shown below equation.

[Q ^([2]) , R]=QRdecomposition [(H ^([32]))⁻¹ H ^([31]) V _([1, . . . , M/2−α]) ^([1]) , W _([M/2−α+1, . . . , M/2]) ^([2])]

[Q ^([3]) , R]=QRdecomposition [(H ^([23]))⁻¹ H ^([21]) V _([1, . . . , M/2−α]) ^([1]), (H ^([13]))⁻¹ H ^([12]) W _([M/2−α+1, . . . , M/2]) ^([2])]

V ^([2]) =Q _([1, . . . , M/2]) ^([2]) , V ^([3]) =Q _([1, . . . , M/2]) ^([3])  [equation 56]

FIG. 7 illustrates feedback information at each receiver for efficient interference alignment method for 3 cell MIMO interference channel with limited feedback.

Referring FIG. 7, Each transmitter transmits pilot signal and each receiver estimates each channel from transmitter. Based on equation 54 and 55 each receiver feeds back its feedback information to target transmitter. For example, receiver 1 feeds back (H^([13]))⁻¹H^([12])W_([M/2−α+1, . . . , M/2]) ^([2]) to transmitter 3. receiver 2 feeds back (H^([23]))⁻¹H^([21])V_([1, . . . , M/2−α]) ^([1]). And receiver 3 feeds back (H^([32]))⁻¹H^([31])V_([1, . . . , M/2−α]) ^([1]). FB^([i→j]) denotes feedback information from receiver i to transmitter j. By using equation 56, transmitter 2 and 3 determine each precoder.

Efficient interference alignment method for 3 cell MIMO interference channel with limited feedback(hereinafter ‘proposed method’) is invented to avoid the product of channel matrices and get better performance. If one node does not use α DOF, IA solution is separated to several independent equation, resulting in avoiding the product of all cross link channel matrices and reducing total feedback overhead.

Table. 1 shows the comparison of feedback amount between conventional method (quantized channel matrix feedback) and proposed method. The proposed method has much small feedback amount compared with conventional method.

TABLE 1 conventional method (channel matrix feedback) proposed method feedback amount 6M² 2M(M/2 − α) + Mα (# of feedback coefficient) M = 4  96 12 M = 8 384 48

FIG. 8 shows sum rate performance, total number of streams of other method.

Referring FIG. 8, the proposed method has remarkable sum rate performance gain compared with other methods. In this result, conventional method denotes naive channel matrix feedback and CSI exchange mehtod is the method mentioned in previous description. Total number of data streams for proposed method is 5 when M=4, others is 6. For this reason, the proposed method is not only more error robust than conventional IA solution but also reduces feedback overhead.

VII. Binary Power Control for Interference Alignment with Limited Feedback

Given finite-rate CSI exchange, quantization errors in CSI cause interference misalignment. To control the resultant interferences, Transmission Power Control (TPC) method is needed. However, the optimal solution for TPC is a non-convex problem.

For the simpler implementation, Binary Power Control (BPC) method is proposed.

BPC method has the advantages of simpler power control algorithms. The sum rate capacity performance of BPC method in 2 users, which it is approaching 99% of the capacity of continuous power control. We expand this into general K multi-antenna user and propose a distributed method for implementing binary power control in decentralized networks.

Binary Power Control is the system that decides the transmit power of transmitter K within P_(k)={0, P_(max)}, which is easily understood as turning on/off system. For the practical use of transmission power control, we propose the method using feedback and feedforward. Consider K=M+1 MIMO user interference channel, where each transmitter and receiver are equipped with M antennas and DoF for each user is d=1. Based on IA condition and precoding vector in equation 50 and 51, we firstly explain error propagation caused by quantization error by CSI exchange.

1. Error propagation caused by quantization by CSI exchange method (K=4, as an example).

In K=4, the beamforming vectors(precoding vectors) with quantization error is shown as below equation.

^([1]) =V ^([1]) +e ^([1])

^([2]) =V ^([2]) +e ^([1]) +e ^([2])

^([3]) =V ^([3]) +e ^([1]) +e ^([2]) +e ^([3])

^([4]) =V ^([4]) +e ^([1]) +e ^([2]) +e ^([3]) +e ^([4])  [equation 57]

In equation 57, e^([1]) is the channel quantization error from initialization of V^([1]), e^([2]) to e^([4]) is the vector quantization error due to the feedback, and

-   ^([k])

is the beamforming vector with the quantized channel matrices. Due to the error propagation, transmitter K causes biggest quantization error and this brings the result of the probability of turning off for each transmitter.

FIG. 9 shows probability of turning off for each transmitter.

Referring FIG. 9, probability of turning off is increased as the index of transmitter is increased due to the error propagation.

Using this result, the BPC method start decision of the power of user K with the ratio γ_(k) expressed as following equation assuming that the receivers has the knowledge of all the interfering channels.

P _(k)=0 if γ_(k)<σ_(k) ² where γ_(k)=Σ_(i=1,i≠k) ^(K) P _(i) U ^([k]) ⁺ H ^([ki]) V ^([i])  [equation 58]

In equation 58, U^([k]) is the received M×1 beamforming vector for receiver k, V^([i]) is the received M×1 beamforming vector for transmitter i, and H^([ki]) is the M×M channel matrix from transmitter i to receiver k, σ_(k) ² represents the AWGN noise variance.

2. A binary power control method.

FIG. 10 shows a binary power control method.

Referring FIG. 10, the binary power control method comprises following steps.

1. step 1—Transmitter K: Receiver K computes γ_(K) using equation 62 and turns off the link (i.e P_(K)=0) if γ_(K)<σ_(K) ² and set the power P_(K)=P_(Max) if γ_(K)≧σ_(K) ².

Then, the receiver K gives the feedback T^([K]) to transmitter K. T^([K]) denotes an indicator bit which indicates on/off. For example, T^([k])=1 if P_(k)=P_(Max) and T^([k])=0 if P_(k)=0. The power for all the other transmitter is set as P_(Max).

2. step 2—Transmitter K−1: Receiver K−1 computes γ_(K−1) and turns off the link if γ_(K−1)<σ_(K−1) ² and set the power P_(K−1)=P_(Max) if γ_(K−1)≧σ_(K−1) ². Then, the receiver K−1 gives the feedback T^([K) ^(−1]) to transmitter K−1. The power of transmitter K is set in the earlier step (step 1) and T^([K]) is feedforwarded to receiver K−1. The power for all the other transmitter is considered as P_(Max). This process is repeated until transmitter 2.

3. step 3—Transmitter 1: Receiver 1 computes γ₁ and decides its power P₁={0, P_(Max)}. The power through transmitter 2 to K is set in earlier steps and feedforwarded to receiver 1.

The described BPC method is using feedback and feedforward starting from K^(th) user. Due to the error propagation, the K^(th) user has the biggest quantization error. And for this reason, the K^(th) transmitter with the most error happens to transmit with power ‘0’ with the biggest probability. Using this result, this BPC method start decision of the power of K^(th) transmitter for P_(k)={0, P_(Max)} with the ratio γ_(k) at the K^(th) receiver since the entire receiver has the knowledge of all the interfering channels. This BPC method need not a central unit to determine the network optimal on/off policy and the power of each trasmitter is determined as distributed manner.

To compare the sum rate capacity performance, binary exhaustive search is used. To observe the sum rate capacity performance by binary exhaustive search, first generate all (2^(K)−1) combinations of transmitters with its power as 0 or P_(Max), and select the combination that maximized the sum rate capacity. In the case of K=4, M=3, the sample of combinations generated are as following. (P_(Max),P_(Max),P_(Max),P_(Max)), (P_(Max),P_(Max),P_(Max),0), . . . , (0, P_(Max),P_(Max),P_(Max)), . . . , (0,0,P_(Max),P_(Max)), . . . , (0, 0, 0, P_(Max)).

FIG. 11 shows the result of the binary power control method.

Referring FIG. 11, When K=4, M=3, feedback bits=6(using random codebook), the binary power control method approaches to 93% of the capacity of exhaustive search when SNR is 20dB, which is nearly optimal. The ideal curve is the result in perfect CSI environment.

VIII. Interference Power Control.

Interference misalignment (IM) due to CSI quantization errors results in residual interference at receivers. The interference power at a particular receiver depends on the transmission power at interferers, their beamformers' IM angles, and the realizations of interference channels. Since all links are coupled, there is a need for regulating the transmission power of transmitters such that the number of links meeting their QoS requirements, namely not in outage, is maximized. A suitable approach is for the receivers to send TPC signals to interferers to limit their transmission power. However, designing TPC algorithms based on this approach is challenging due to the dependence of interference power on several factors as mentioned above and the required knowledge of global CSI and QoS requirements.

The TPC signals sent by a receiver provide upper bounds on the transmission power of interferers (i.e. transmitter). First the effect of IM is described and then the method for transmitting a TPC signal for an interference power control is described.

1. The effect of IM.

The IM due to CSI quantization errors results in residual interference at receivers as explained by the following example.

Consider the 3-user interference channel where the symbol extension (number of subchannels) is (2n+1)=3. Given v^([2])=1_(3×1) and based on IA condition, the 3×1 beamforming vectors (v₁ ^([1]),v₂ ^([1]),v^([2]),v^([3])) satisfy the following constraints in equation 59.

H^([12])v^([2])=H^([13])v^([3])

H^([23])v^([3])=H^([21])v₁ ^([1])

H^([32])v^([2])=H^([31])v₂ ^([1])  [equation 59]

Here, the beamforming vector refers to weights applied to signals transmitted over different subchannels rather than antennas in the case of a multi-antenna system.

Generalizing the above constraints to an arbitrary n and using zero-forcing linear receivers are sufficient to guarantee the optimality in terms of DOF, namely each user achieves the optimal number of DOF equal to ½ per symbol as n converges to infinity. For the current example of n=1, the 3×1 zero-forcing beamforming vectors, denoted as (f₁ ^([1]), f₂ ^([1]), f^([2]), f^([3])), satisfy the following constraints equation.

f₁ ^([1])∈null[H^([11])v₂ ^([1]), H^([12])v^([2])]

f₂ ^([1])∈null[H^([11])v₁ ^([1]), H^([12])v^([2])]

f^([2])∈null[H^([21])v₁ ^([1]), H^([21])v₂ ^([1])]

f^([3])∈null[H^([31])v₁ ^([1]), H^([32])v^([2])]  [equation 60]

It follows that the receive signals after zero-forcing equalization are interference-free and given as below equation.

{circumflex over (x)} ₁ ^([1])=(f ₁ ^([1]))^(†) H ^([11]) v ₁ ^([1]) x ₁ ^([1])+(f ₁ ^([1]))^(†) z ^([1])

{circumflex over (x)} ₂ ^([1])=(f ₂ ^([1]))^(†) H ^([11]) v ₂ ^([1]) x ₂ ^([1])+(f ₂ ^([1]))^(†) z ^([2])

{circumflex over (x)} ^([2])=(f ^([2]))^(†) H ^([22]) v ^([2]) x ^([2])+(f ^([2]))^(†) z ^([2])

{circumflex over (x)} ^([3])=(f ^([3]))^(†) H ^([33]) v ^([3]) x ^([3])+(f ^([3]))^(†) z ^([3])  [equation 61]

In equation 61, z are the samples of the AWGN processes.

Let

-   ({circumflex over (v)}₁ ^([1]),{circumflex over (v)}₂     ^([1],)v^([3]))

denote the quantized version of beamforming vectors (v₁ ^([1]), v₂ ^([1]), v^([3])). Due to CSI quantization errors, the IA conditon in equation 59 are no longer satisfied. This is expressed in equation 62.

H^([12])v^([2])≠H^([13)]{circumflex over (v)}^([3])

H^([23]){circumflex over (v)}^([3])≠H^([21]){circumflex over (v)}₁ ^([1])

H^([32])v^([2])≠H^([31]){circumflex over (v)}₂ ^([1])  [equation 62]

Consequently, nulling interference using zero-forcing beamforming vector is infeasible since it requires below equation.

H^([12])v^([2])=H^([13]){circumflex over (v)}^([3])

H^([23]){circumflex over (v)}^([3])=H^([21]){circumflex over (v)}₁ ^([1])

H^([32])v^([2])=H^([31]){circumflex over (v)}₂ ^([1])  [equation 63]

It follows that the receive signals after zero-forcing equalization are given as below equation.

{circumflex over (x)} ₁ ^([1])=(f ₁ ^(1|))^(†) H ¹¹ {circumflex over (v)} ₁ ^([1]) x ₁ ^(|1|)+(f ₁ ^(|1|))^(†)(H ^(|11|) {circumflex over (v)} ₂ ^([1]) x ₂ ^(|1|) +H ^(|12|) {circumflex over (v)} ^([2]) x ^(|2|) −H ^(|13|) {circumflex over (v)} ^([3]) x ^(|3|))+(f ₁ ^(|1|))^(†) z ^(|1)

{circumflex over (x)} ₂ ^([1])=(f ₂ ^([1]))^(†) H ^([11]) {circumflex over (v)} ₂ ^([1]) x ₂ ^([1])+(f ₁ ^([1]))^(†)(H ^([11]) {circumflex over (v)} ₁ ^([1]) x ₁ ^([1]) +H ^([12]) {circumflex over (v)} ^([2]) x ^([2]) −H ^([13]) {circumflex over (v)} ^([3]) x ^([3]))+(f ₂ ^([1]))^(†) z ^([2])

{circumflex over (x)} ^([2])=(f ^(2|))^(†) H ²² {circumflex over (v)} ^([2]) x ^(|2|)+(f ₁ ^(|2|))^(†)(H ^(|21|) {circumflex over (v)} ₁ ^([1]) x ^(|1|) +H ^(|21|) {circumflex over (v)} ₂ ^([1]) x ₂ ^(|2|) −H ^(|23|) {circumflex over (v)} ^([3]) x ^(|3|))+(f ^(|2|))^(†) z ^(|2)

{circumflex over (x)} ^([3])=(f ^([3[))^(†) H ^([33]) {circumflex over (v)} ^([3]) x ^([3])+(f ₁ ^([1]))^(†)(H ^([31]) {circumflex over (v)} ₁ ^([1]) x ^([1]) +H ^([31]) {circumflex over (v)} ₂ ^([1]) x ₂ ^([1]) −H ^([32]) {circumflex over (v)} ^([2]) x ^([2]))+(f ^([1]))^(†) z ^([3])  [equation 64]

In equation 64,

-   (f₁ ^([1]))^(†)(H^([11]){circumflex over (v)}₂ ^([1])x₂     ^([1])+H^([12]){circumflex over     (v)}^([2])x^([2])+H^([13]){circumflex over (v)}^([3])x^([3]))

,

-   (f₁ ^([1]))^(†)(H^([11]){circumflex over (v)}₁ ^([1])x₁     ^([1])+H^([12]){circumflex over     (v)}^([2])x^([2])+H^([13]){circumflex over (v)}^([3])x^([3]))

,

-   (f₁ ^([2]))^(†)(H^([21]){circumflex over (v)}₁     ^([1])x^([1])+H^([21]){circumflex over (v)}₂ ^([1])x₂     ^([1])+H^([23]){circumflex over (v)}^([3])x^([3]))

,

-   (f₁ ^([1]))^(†)(H^([31]){circumflex over (v)}₁     ^([1])x^([1])+H^([31]){circumflex over (v)}₂ ^([1])x₂     ^([1])+H^([32]){circumflex over (v)}^([2])x^([2]))

are residual interferences and z are the samples of the AWGN processes. The residual interferences in the above received signals are nonzero since it is infeasible to choose receiver beamforming vectors in the null spaces of the interference.

2. The method for transmitting a TPC signal for an interference power control.

We propose the method for transmitting a TPC signal between interferers and receivers such that the users' QoS constraints are satisfied. First, to compute the TPC signals, the users' QoS constraints can be translated into constraints on the interference power that are linear functions of the interferers' transmission power as elaborated below.

FIG. 12 shows effect of interference misalignment due to quantization of feedback CSI.

The quantization errors in feedback CSI causes IM at receivers. Specifically, at each receiver, interference leaks into the data subspace even if linear zero-forcing interference cancellation is applied as illustrated in FIG. 12.

Referring FIG. 12, the length of an arrow for a particular interferer depends on the product of its transmission power and the interference channel. As observed from FIG. 12, the power of each residual interference depends on the transmission power of the corresponding interferer, the misalignment angels of IA beamforming vectors(beamformer), and the interference channel power. Thus, the method for transmitting a TPC signal is to generate TPC signals based on the factors including the misalignment angels of IA beamformers and the interference channel power.

Let the power of the residual interference from the interferer n to the receiver m be denoted by the function P^([n])f(θ^([mn])) where P represent the transmission power of the transmitter n and θ^([mn ])represent the IM angel for the CSI sent from the receiver m to the transmitter n, and f(θ^([mn]))≦sin²(θ^([mn])). In this case, the SINR at the receiver m can be written as below equation.

$\begin{matrix} {\min\limits_{\{\theta^{k}\}}{\sum\limits_{k = 1}^{K}{{B^{\lbrack k\rbrack}\left( \theta^{\lbrack k\rbrack} \right)}\mspace{14mu} {s.t.\mspace{14mu} {constraint}}\mspace{14mu} {in}\mspace{14mu} {equation}\mspace{14mu} 65\mspace{14mu} {\forall m}}}} & \left\lbrack {{equation}\mspace{14mu} 65} \right\rbrack \end{matrix}$

In equation 65, G^([mm]) represents the gain of the data link after transmit and receive beamforming and σ² represents the AWGN noise variance. The QoS requirement for the m^(th) user can be quantified by an outage threshold β^([m]) at the m^(th) receiver where SINR^([m])≧β^([m]) is required for correct data decoding. This leads to a constraint on the interference power expressed in below equation.

$\begin{matrix} {\min\limits_{\{ P^{\lbrack k\rbrack}\}}{\lim\limits_{T\rightarrow\infty}{\frac{1}{T}{\sum\limits_{t = 1}^{T}{\sum\limits_{k = 1}^{K}{{B^{\lbrack k\rbrack}\left( \theta_{t}^{\lbrack k\rbrack} \right)}\mspace{14mu} {s.t.\mspace{14mu} {constraint}}\mspace{14mu} {in}\mspace{14mu} {equation}\mspace{14mu} 65\mspace{14mu} {\forall m}}}}}}} & \left\lbrack {{equation}\mspace{14mu} 66} \right\rbrack \end{matrix}$

The above constraints on the residual interference's power can be used to compute TPC signals sent by the n^(th) receiver for limiting the transmission power {P^([m])|m≠n}.

Second, the sequential TPC signal exchange between subsets of interferers and interfered receivers is necessary for maximize the number of users who meet their QoS requirements. It is important to note that what matters to a receiver is the sum residual interference power but this receiver needs to control individual interference components by TPC signal. Consequently, a challenge in computing TPC signals is to resolve the dilemma for a receiver that applies too stringent TPC on a particular interferer may cause its data link to be in outage and too loose TPC may cause the interferer to have strong interference on another receiver. Thus TPC exchange provides a decentralized mechanism for receivers to handshake and reach an optimally control of the transmission power of all transmitters. Such TPC signal exchange will exploit the method of CSI exchange methods discussed earlier.

IX. Interference Misalignment Control (IMC).

The power of the residual interference at a receiver due to IM depends on IM angles besides interferers' transmission. The IM angles can be directly controlled by varying the quantization resolutions of feedback CSI. Despite serving the same purpose, IM control (IMC) differs from TPC in the following aspects.

1. IMC is integrated with cooperative CSI exchange and directly affects the amount of CSI overhead. In contrast, TPC is performed after cooperative CSI exchange and involves transmission of scalars rather than vectors/matrices for CSI exchange.

2. The design of IMC algorithms depend on the designs of hierarchical codebooks for

CSI quantization, which are irrelevant for TPC.

3. IMC algorithms will be designed to enable CSI compression in time. Such compression is not directly applicable for TPC signals.

In the preceding description on CSI exchange methods, fixed-resolution CSI quantizers are assumed. IMC is an enhanced feature for CSI exchange methods supporting flexible feedback CSI resolutions to improve feedback efficiency. The IMC method will be designed based on the principle that to constrain the power of a particular interference component the corresponding IM angle has to be smaller (more feedback bits) for larger interference channel power and vice versa. IMC requires the employment of quantizers using hierarchical codebooks each of which comprises a set of sub-codebooks with different sizes. There exists a rich literature for designing such codebooks. The IMC method focus on distributed IMC algorithms for IA which is an open area. Specifically, such algorithms will be designed to minimize the network CSI overhead under QoS constraints for different users. One basic principle proposed in this IMC method is to use lower resolution quantizer for an interferer having low residual interference power (i.e., small misalignment angels of IA beamformers, low interferer transmission power, and/or low interference channel power) and to use higher resolution quantizer for an interferer having high residual interference power (i.e., large misalignment angels of IA beamformers, high interferer transmission power, and/or high interference channel power).

Furthermore, the IMC method is proposed to compress feedback by exploiting channel temporal correlation and using tools from stochastic optimization.

The IMC method is based on the relation between interference power, IMC angles and the number of feedback bits. In particular, the interference power approximately proportional to squared-cosine of IM angle which is an exponential function of the number of feedback bits divided by the CSI dimensionality.

Building on these results, we propose a decentralized IMC method discussed as follows. Let B^([k])(θ^([k])) denote the number of bits required for feedback and feedforward of the beamformer v^([k])

with the resolution specified by θ^([k]). The decentralized IMC method is designed to solve the following problem of minimizing total feedback overhead under a set of QoS constraints expressed by below equation.

$\begin{matrix} {\min\limits_{\{\theta^{\lbrack k\rbrack}\}}{\sum\limits_{k - 1}^{K}{{B^{\lbrack k\rbrack}\left( \theta^{\lbrack k\rbrack} \right)}\mspace{14mu} {s.t.\mspace{14mu} {constraint}}\mspace{14mu} {in}\mspace{14mu} {equation}\mspace{14mu} 65\mspace{14mu} {\forall m}}}} & \left\lbrack {{equation}\mspace{14mu} 67} \right\rbrack \end{matrix}$

Given the optimal set of θ^([k]), the feedback bits are allocated accordingly by tuning the resolutions of CSI quantizers. Designing the decentralized IMC method is challenging due to the coupling of data links. Exactly solving the above optimization problem by decentralized IMC method may require exchange of additional CSI and QoS information between receivers.

We propose the method for reducing feedback by exploiting channel temporal correlation. Specifically, the objective function of optimization problem in equation 67 is modified to be the time-average feedback overhead expressed in equation 68.

$\begin{matrix} {\min\limits_{\{ P^{\lbrack k\rbrack}\}}{\lim\limits_{T\rightarrow\infty}{\frac{1}{T}{\sum\limits_{t = 1}^{T}{\sum\limits_{k = 1}^{K}{{B^{\lbrack k\rbrack}\left( \theta_{t}^{\lbrack k\rbrack} \right)}\mspace{14mu} {s.t.\mspace{14mu} {constraint}}\mspace{14mu} {in}\mspace{14mu} {equation}\mspace{14mu} 65\mspace{14mu} {\forall m}}}}}}} & \left\lbrack {{equation}\mspace{14mu} 68} \right\rbrack \end{matrix}$

In equation 68, P^([k]) represents the IMC policy at the kth receiver that chooses a particular feedback resolution (or sub-codebook size) based on available CSI and generates θ_(t) ^([k]). The above problem in equation 68 is a stochastic optimization problem. Based on Markov channel model, this problem can be solved efficiently using dynamic programming, resulting in decentralized IMC method.

While the invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. 

1. A feedback method for interference alignment in wireless network having K transmitters and K receivers, the method comprising: transmitting, performed by transmitter n−1, a precoding vector of transmitter n−1(n is a integer between 2 and K−1) to receiver n+1; calculating, performed by the receiver n+1, a precoding vector of transmitter n using the precoding vector of transmitter n−1; and transmitting, performed by the receiver n+1, the precoding vector of transmitter n to transmitter n.
 2. The method of claim 1, further comprising; transmitting, performed by transmitter K−1, a precoding vector of transmitter K−1 to receiver 1; calculating, performed by the receiver 1, a precoding vector of transmitter K; and, transmitting, performed by receiver 1, its own precoding vector to transmitter K
 3. The method of claim 1, further comprising: transmitting, performed by transmitter K−1, a precoding vector of transmitter K−1 to receiver 1; calculating, performed by the receiver 1, a precoding vector of transmitter K; and, transmitting, performed by the receiver 1, the precoding vector of the transmitter K.
 4. The method of claim 1, wherein K is 4 and each of the K transmitters and the K receivers has 3 antennas.
 5. A feedback method for the interference alignment in wireless network having K(K is integer larger than 1) transmitters and K receivers, the method comprising: transmitting, performed by transmitter n−1(n is an integer between 3 and K), a precoding vector of transmitter n−1 to receiver n−2; and calculating, performed by the receiver n−2, a precoding vector of transmitter n; and transmitting, performed by the receiver n−2, the precoding vector of transmitter n to transmitter n−1.
 6. The method of claim 1, further comprising: transmitting, performed by receiver K, a channel matrix to receiver K-1; transmitting, performed by the receiver K−1, a precoding vector of transmitter 1 which is calculated based on the channel matrix to transmitter 1; transmitting, performed by the transmitter 1, the precoding vector of transmitter 1 to receiver K; calculating, performed by the receiver K, a precoding vector of transmitter 2 based on the precoding vector of transmitter 1; and transmitting, performed by the receiver K, the precoding vector of transmitter 2 to transmitter
 2. 7. The method of claim 5, wherein K is 4 and each of the K transmitters and the K receivers has 3 antennas.
 8. A feedback method for the interference alignment in wireless network having base station(BS), K(K is integer larger than 1) transmitters and K receivers, the method comprising: transmitting, performed by the BS, a signal dicicating interferers which are aligned on the same dimension at each receiver of the K receivers to the each receiver; receiving, from the each receiver, channel status information; determining, performed by the BS, precoding vectors for the K transmitters; and transmitting, performed by the BS, the determined precoding vectors to each transmitter of the K transmitters.
 9. The method of claim 8, wherein the channel status information comprises one interference matrix multiplied by the inverse of another interference matrix.
 10. The method of claim 9, wherein the one interference matrix indicates a channel status between a receiver and first transmitter and the inverse of another interference matrix indicates a channel status between the receiver and second transmitter, and the first transmitter and the second transmitter are indicated by the signal. 